US5748775A - Method and apparatus for moving object extraction based on background subtraction - Google Patents

Method and apparatus for moving object extraction based on background subtraction Download PDF

Info

Publication number
US5748775A
US5748775A US08/401,972 US40197295A US5748775A US 5748775 A US5748775 A US 5748775A US 40197295 A US40197295 A US 40197295A US 5748775 A US5748775 A US 5748775A
Authority
US
United States
Prior art keywords
sub
region
image
change
background
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
US08/401,972
Other languages
English (en)
Inventor
Megumu Tsuchikawa
Atsushi Sato
Akira Tomono
Kenichiro Ishii
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nippon Telegraph and Telephone Corp
Original Assignee
Nippon Telegraph and Telephone Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nippon Telegraph and Telephone Corp filed Critical Nippon Telegraph and Telephone Corp
Assigned to NIPPON TELEGRAPH AND TELEPHONE CORPORATION reassignment NIPPON TELEGRAPH AND TELEPHONE CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: SATO, ATSUSHI, TSUCHIKAWA, MEGUMU
Application granted granted Critical
Publication of US5748775A publication Critical patent/US5748775A/en
Anticipated expiration legal-status Critical
Expired - Fee Related legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/277—Analysis of motion involving stochastic approaches, e.g. using Kalman filters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/103—Static body considered as a whole, e.g. static pedestrian or occupant recognition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/20—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using video object coding

Definitions

  • the present invention relates to a method and an apparatus for extracting a moving object in the image sequence by using a subtraction between an input image and a background image, which can stably extract the moving object region even under an environment which incorporates illumination changes.
  • Conventionally known methods for extracting a moving object based on image processing include: (1) a method for storing a reference background image, extracting difference data by a subtraction between the input image and the background image, and obtaining the moving object by means of the binarization of the difference data using a threshold; (2) a method for obtaining data on difference between frames by a subtraction between the input image and an immediately previous frame image as a reference image, and obtaining the moving object by means of the binarization of the obtained data; (3) a method for obtaining correspondences between changing points in the reference image and the input image by calculating quantities such as motion vectors, and obtaining the moving object as a set of moved points; (4) a method for obtaining a change between the reference image and the input image according to a correlation within a target region, and obtaining the moving object as a changed region; and (5) a method for carrying out a (shape) recognition and a tracking of a moving target.
  • the methods based on subtraction have an advantage that the moving object can be extracted at high speed by means of a relatively simple processing, and widely used in various fields such as the industrial product inspection and measurement, the automobile measurement, and the monitoring system.
  • FIG. 1 shows an outline of such a conventional method for extracting the moving object based on background subtraction, where the moving object is extracted by obtaining a difference between a reference image Y representing a fixed background image and a latest input image Xi, and judging a region at which the obtained difference is greater than or equal to a prescribed threshold as the moving object in motion.
  • the moving object can be extracted easily under a circumstance in which the background image does not change, but when there is an illumination change, the reference background image also changes accordingly such that the difference obtained in the above procedure can be significantly large for the background portion as well and it becomes impossible to extract the moving object stably.
  • FIG. 2 shows a flow chart for the operation in such a moving object extraction based on background subtraction incorporating the background updating.
  • a target image input processing 811 enters the frame images sequentially.
  • a background change judgement processing 812 checks whether there is a change in the background values for the input image Xi, and whenever there is a change, a background image correction processing 813 updates the background image accordingly.
  • a background subtraction processing 814 obtains the difference data between the input image Xi and the updated background image, and a binarization processing 815 binarizes the obtained difference data by using a prescribed threshold, so as to output a moving object output 816 representing a region in the input image Xi specified by the result of the binarization processing 815 as the moving object.
  • the conventionally used schemes for updating the background image at the background image correction processing 813 include a scheme for using a weighted sum of the input image values and the stored background image values, and a scheme for using a straightforward mean of the frame image values for immediately previous several frames.
  • the change in the background values is judged without distinguishing a change due to a passing of the moving object and a change due to the illumination change, so that there has been a problem that the background image can be updated erroneously when many moving objects pass through the input image.
  • a method of moving object extraction based on background subtraction comprising the steps of: (a) sequentially entering input images containing a moving object region to be extracted; (b) storing temporal changes of image feature parameter values for sub-regions subdividing a frame of each input image entered at the step (a); (c) statistically processing a temporal change of the image feature parameter values for each sub-region within a prescribed target region of the frame stored at the step (b) over a prescribed period of time to to obtain at least one statistical quantity characterizing said temporal change, judging whether said temporal change is due to an illumination change or not according to said statistical quantity and a prescribed illumination change judging condition, and updating a background image value for said each sub-region by a new background image value according to the image feature parameter values for said each sub-region during the prescribed period of time t 0 , so as to obtain a reconstructed background image; (d) applying a subtraction processing to one of the input images entered at the step (a) and the
  • an apparatus for moving object extraction based on background subtraction comprising: input means for sequentially entering input images containing a moving object region to be extracted; storage means for storing temporal changes of image feature parameter values for sub-regions subdividing a frame of each input image entered by the input means; background update means for statistically processing a temporal change of the image feature parameter values for each sub-region within a prescribed target region of the frame stored by the storage means over a prescribed period of time to to obtain at least one statistical quantity characterizing said temporal change, judging whether said temporal change is due to an illumination change or not according to said statistical quantity and a prescribed illumination change judging condition, and updating a background image value for said each sub-region by a new background image value according to the image feature parameter values for said each sub-region during the prescribed period of time t 0 , so as to obtain a reconstructed background image; subtraction means for applying a subtraction processing to one of the input images entered by the input means and the reconstructed background image obtained by
  • FIG. 1 is a schematic diagram indicating an outline of a conventional method of moving object extraction based on background subtraction.
  • FIG. 2 is a flow chart for the operation in a conventional method of moving object extraction based on background subtraction.
  • FIG. 3 is a block diagram showing a system configuration of a moving object extraction system in the first embodiment of the present invention.
  • FIG. 4 is a block diagram showing a detailed functional configuration of the moving object extraction system of FIG. 3.
  • FIG. 5A is an illustration of an exemplary input image with a slit used in a moving object extraction system in the second embodiment of the present invention.
  • FIG. 5B is an illustration of an exemplary space-time image obtained from the input image of FIG. 5A.
  • FIG. 5C is an illustration of an exemplary graph indicating temporal change of input value and background value obtained from the space-time image of FIG. 5B.
  • FIG. 5D is an illustration of an exemplary space-time image indicating the extraction result obtained from the space-time image of FIG. 5B.
  • FIG. 6 is a block diagram showing a system configuration of a moving object extraction system in the fourth embodiment of the present invention.
  • FIG. 7 is a block diagram showing a detailed functional configuration of a background image region reconstruction means in the moving object extraction system of FIG. 6.
  • FIG. 8 is a block diagram showing a detailed functional configuration of a background image region reconstruction means in a moving object extraction system in the fifth embodiment of the present invention.
  • FIGS. 9A and 9B are graphs three-dimensional feature vector space for explaining the operation in a moving object extraction system in the fifth embodiment of the present invention.
  • FIG. 10 is a block diagram showing a schematic configuration of a background image sub-region update means in a moving object extraction system in the sixth embodiment of the present invention.
  • FIG. 11 is a block diagram showing a detailed functional configuration of the background image sub-region update means of FIG. 10.
  • FIG. 12 is a block diagram showing a system configuration of a moving object extraction system in the seventh embodiment of the present invention.
  • FIGS. 13A and 13B are diagrams of input image sequences for explaining a difference between the first and seventh embodiments of the present invention.
  • FIGS. 14A and 14B are graphs of temporal change of intensity value for explaining a difference between the first and seventh embodiments of the present invention.
  • FIG. 15 is a block diagram showing a system configuration of a moving object extraction system in the eighth embodiment of the present invention.
  • FIGS. 16A, 16B, and 16C are graphs showing exemplary threshold settings used in the moving object extraction system of FIG. 15.
  • FIG. 17 is a block diagram of an exemplary physical configuration for an apparatus corresponding to the first embodiment of the present invention.
  • FIG. 18 is a block diagram of an exemplary physical configuration for an apparatus corresponding to the second embodiment of the present invention.
  • FIG. 19 is a block diagram of an exemplary physical configuration for an apparatus corresponding to the seventh embodiment of the present invention.
  • FIG. 20 is a block diagram of an exemplary physical configuration for an apparatus corresponding to the eighth embodiment of the present invention.
  • FIG. 21 is a block diagram of a modified physical configuration for an apparatus corresponding to the first embodiment of the present invention.
  • FIG. 22 is a block diagram of a further modified physical configuration for an apparatus corresponding to the first embodiment of the present invention.
  • FIGS. 3 and 4 the first embodiment of the moving object extraction based on background subtraction according to the present invention will be described in detail.
  • FIG. 3 shows a system configuration of a moving object extraction system in this first embodiment
  • FIG. 4 shows a detailed functional configuration of the moving object extraction system of FIG. 3.
  • the system generally comprises: a camera 001 for entering an image sequence of the input images; an image feature parameter value temporal change storage means 100 including a plurality of frame image memories 101, 102, etc. for storing image feature parameter values for the sequentially entered input images; a background image region reconstruction means 300 for reconstructing the background image according to the temporal change of the stored image feature parameter values; and a moving object extraction means 500 for obtaining a moving object output 520 representing the moving object from the entered input image and the reconstructed background image.
  • each frame image memory in the image feature parameter value temporal change storage means 100 stores the image feature parameter values for each input image containing a background region 110 and a moving object region 120 which is divided into a plurality of sub-regions a k such as pixels located at coordinate positions (x, y) within each frame.
  • a k such as pixels located at coordinate positions (x, y) within each frame.
  • an intensity at each pixel is used as an exemplary image feature parameter at each sub-region a k .
  • the background image region reconstruction means 300 further comprises a plurality of background image sub-region update means 200 provided in correspondence to a plurality of sub-regions a k for updating the image feature parameter value of each sub-region a k
  • each background image sub-region update means 200 further includes an intensity change statistical processing means 210 (211, 212 in FIG. 4 for sub-regions a 1 , a 2 ) for statistically processing the temporal change of the intensity at each sub-region during a prescribed period of time t 0 , an illumination change judging condition judgement means 220 (221, 222 in FIG.
  • a sub-region with a value "1" in the reconstructed background image 310 represents the updated sub-region while a sub-region with a value "0" in the reconstructed background image 310 represents the unchanged (not updated) sub-region.
  • the intensity change statistical processing means 211 (212) obtains a histogram 213 (214) of occurrences of intensity values at a pixel a 1 (a 2 ) and detect a peak and a variance ⁇ in the histogram 213 (214) as the statistical feature parameter, while the illumination change judging condition judgment means 221 (222) makes the judgment by comparing the detected variance ⁇ with a predetermined variance ⁇ 0 indicating a threshold for separating a case due to the illumination change from a case due to the passing of the moving object.
  • the moving object extraction means 500 further comprises a subtraction means 400 for subtracting the reconstructed background image obtained by the background image region reconstruction means 300 from the input image entered from the camera 001 in units of pixels, and a binarization means 510 for applying a binarization using a prescribed threshold to a result of the subtraction obtained by the subtraction means 400, and outputting the moving object output 520.
  • the monochromatic image sequence of the input images are entered from the camera 001, and stored into the frame image memories 101, 102, etc. in the image feature parameter value temporal change storage means 100, where each input image contains a sub-region a k centered around a coordinate position (x, y).
  • This sub-region a k may be a region comprising a plurality of pixels, but it is assumed to be a single pixel for the sake of simplicity in this first embodiment.
  • the image feature parameter at each sub-region is the intensity at each pixel given by 8 bit intensity value in this first embodiment.
  • the intensity change statistical processing means 211 obtains the histogram 213 of occurrences of intensity values for the sub-region a 1 as shown in FIG. 4 by taking intensity values on a horizontal axis and a frequency of occurrences on a vertical axis.
  • this sub-region a 1 remains as a part of the background as it does not contain the moving object during the period of time t 0 .
  • the intensity of the background mainly changes due to the illumination change, but when the period of time t 0 is as short as several seconds, the illumination change during such a short period of time is generally small, so that the distribution of the intensity values in the histogram 213 takes a bell shape with a small variance ⁇ around the peak.
  • the intensity change statistical processing means 212 obtains the histogram 214 of occurrences of intensity values for the sub-region a 2 as shown in FIG. 4, and this sub-region a 2 contains the moving object so that the intensity values largely change during the period of time t 0 and the distribution of the intensity values in the histogram 214 takes a spread shape with a large variance ⁇ around the peak.
  • each of the illumination change judging condition judgment means 221 and 222 compares the variance ⁇ obtained by the respective one of the intensity change statistical processing means 211 and 212 with the predetermined variance ⁇ 0 indicating a threshold for separating a case due to the illumination change from a case due to the passing of the moving object.
  • the obtained variance ⁇ is less than the predetermined variance ⁇ 0 , the change of the intensity value at the respective sub-region is judged as due to the illumination change, in which case it is decided that the intensity value at the respective sub-region is to be updated.
  • the change of the intensity value at the respective sub-region is judged as due to the cause other than the illumination change, such as the passing of the moving object, in which case it is decided that the intensity value at the respective sub-region is not to be updated.
  • the value update means 231 and 232 update the intensity of the sub-regions a 1 and a 2 whenever necessary.
  • the reconstructed background image 310 is obtained by the background image sub-region update means 200 in units of sub-regions. This operation for updating the background image sub-region may be carried out over the entire frame of each input image, or within a desired region of each input image.
  • the predetermined variance ⁇ 0 as the illumination change judging condition as described above, it is also possible to use a predetermined feature in the distribution shape of the histogram for a case of the illumination change as the illumination change judging condition such that a case of the illumination change can be judged by comparing a feature in the distribution shape of the obtained histogram with the predetermined feature.
  • the manner of updating the background image as described above is also applicable to a case other than the above described case of the pixel value change due to the illumination change. Namely, it is applicable to a case of gradually changing background object, or any other case which is clearly distinguishable from the change due to the passing of the moving object.
  • positions of papers on desks can change irregularly, in clear distinction from the movements of the persons. That is, the papers move discontinuously as they remain unchanged after they are moved once until they are moved again, whereas the movements of the persons are generally continuous during the period of time t 0 .
  • the reconstructed background image 310 obtained in this manner can be considered as reflecting the most reliable state of the background immediately before the moving object appears.
  • the sequentially reconstructed background image at the moving object extraction means 500 in applying the subtraction processing and the binarization processing to the desired region in the input images by the subtraction means 400 and the binarization means 510, it is possible to extract the moving object region 120, and by sequentially repeating this operation with respect to the sequentially entered input images, it is possible to obtain the moving object output 520 representing sequential images of the moving object as shown in FIG. 4.
  • the use of the monochromatic camera and the intensity value at each pixel as the image feature parameter value as described above is only an example, and it is possible to modify the above described first embodiment to use the other image feature parameter such as image concentration data obtained by a monochromatic camera, or data that can be extracted from color data obtained by a color camera such as the intensity, the hue, the saturation, the intensity gradient with respect to neighboring pixels, and other quantities expressed in the chromaticness scale system. It is also possible to use concentration data or temperature data obtained by an infrared camera, distance data obtained by a range sensor, or reflection intensity data obtained by an ultrasonic sensor as the image feature parameter.
  • the sub-region may not necessarily be a single pixel as described above, and may be a block image formed by a plurality of pixels, in which case, the image feature parameter can be a value obtained by statistically processing a plurality of intensity values or other quantities qualifying as the image feature parameter, such as the mean value, the most frequent value, the maximum value, or the minimum value of the intensity values within the block image.
  • the operation up to the illumination change judging condition judgment means 221 and 222 are similar as described above, and the updating of the value at each sub-region in a case it is judged as a case of the illumination change can be carried out as follows. Namely, the block image corresponding to the intensity value at the peak of the histogram of occurrences of intensity values for this sub-region is selected from the frame images for the past period of time t 0 stored in the image feature parameter value temporal change storage means 100, and the background value of this sub-region is replaced by the selected block image in the reconstructed background image 310. When this operation is carried out over the desired region, the reconstructed background image 310 can be obtained sequentially as described above.
  • the operation at the moving object extraction means 500 is similar as described above.
  • FIGS. 5A to 5D the second embodiment of the moving object extraction based on background subtraction according to the present invention will be described in detail.
  • the desired region for applying the operation for updating the background value has been assumed as the entire image frame, but in this second embodiment, this desired region is set to be a single line within an image frame, i.e., a sampling slit 720 in a form of a slit shaped pixel sequence within an image sequence 710 as shown in FIG. 5A.
  • FIG. 5B shows a space-time image 730 formed by the slit 720 in the image sequence 710 of FIG. 5A and a time axis
  • FIG. 5C shows a graph 740 indicating the temporal change of the input value 742 and the background value 741 at a specific sampling position in the space-time image of FIG. 5B
  • FIG. 5D shows a space-time image 750 indicating the extraction result of the moving object 751.
  • the sub-region is set to be a pixel in the image
  • the desired region for applying the updating of the background value is set to be a single line of the sampling slit 720.
  • the space-time image 730 formed by the slit 720 and the time axis can be produced easily at high speed.
  • the image sequence 710 to be entered may not necessarily be a two dimensional image as shown in FIG. 5A, and can be a one dimensional slit image directly obtained by a range sensor or a line camera.
  • the slit 720 is provided over the image sequence 710 containing passing persons which incorporates the illumination changes, and the obtained slit images are arranged along the time axis to obtain the space-time image 730 shown in FIG. 5B.
  • the appropriate background value 741 can be sequentially judging in accordance with the illumination changes, without being influenced by the change of the input value 742 due to the passing of the persons, by means of the proper updating of the background value as in the first embodiment described above.
  • the space-time image 750 with only the moving object 751 extracted as shown in FIG. 5D can be obtained stably at high speed, by means of the moving object extraction based on background subtraction as in the first embodiment described above.
  • the variance ⁇ is compared with the predetermined variance ⁇ 0 in judging the illumination change, but in this third embodiment, either as a replacement of this or as a supplement of this, the following scheme is adopted. Namely, in general, the change of the background image due to the causes other than the passing of the moving object such as the illumination change and the gradually changing background object takes place gradually, and the shapes of the histograms for such cases resemble each other.
  • the shape of the histogram for each sub-region a i is compared with the shapes of the histograms for the other sub-regions in vicinity of that sub-region a i , and it is decided that the updating is necessary for that sub-region a i when the similar histogram shapes are dominant in vicinity of that sub-region a i .
  • the histogram shapes for the sub-regions a 1 , a 4 and as will be similar to each other, so that the intensity changes in these sub-regions will be judged as those due to the illumination changes and therefore the background values at these sub-regions will be updated, whereas the histogram shapes for the sub-regions a 2 and a 3 will be largely different from the others, so that the intensity changes in these sub-regions will be judged as those due to the passing of the moving object and therefore the background values at these sub-regions will not be updated.
  • FIG. 6 shows a system configuration of a moving object extraction system in this fourth embodiment
  • FIG. 7 shows a detailed functional configuration of the background image region reconstruction means 330 in the moving object extraction system of FIG. 6.
  • the system generally comprises: a color camera 002 for entering an image sequence of the color input images; an image feature parameter value temporal change storage means 130 including three sets of frame image memories 131, 132, and 133 for storing image feature parameter values for R, G, and B images in the sequentially entered color input images; a background image region reconstruction means 330 for reconstructing the background image according to the temporal change of the stored image feature parameter values; and a moving object extraction means 530 for obtaining a moving object region 120 from the entered input image and the reconstructed background image.
  • each frame image memory in the image feature parameter value temporal change storage means 130 stores the image feature parameter values for each input image containing a background region 110 and a moving object region 120 which is divided into a plurality of sub-regions a k such as pixels located at coordinate positions (x, y) within each frame.
  • the background image region reconstruction means 330 further comprises a plurality of background image sub-region update means 204 provided in correspondence to a plurality of sub-regions a k for updating the image feature parameter value of each sub-region a k
  • the background image sub-region update means 204 receives a vector set 251 (252) during the period of time t 0 at the respective sub-region a 1 (a 2 ), and includes a statistical processing means 261 (262) for statistically processing distances W among the feature vectors in the vector set 251 (252), an illumination change judging condition judgement means 271 (272) for judging a need for updating the image feature parameter value at each sub-region due to an occurrence of the illumination change according to the statistical processing result at each sub-region, and a value update means 281 (282) for updating the image feature parameter value at each sub-region according to the judgment result of the illumination change judging condition judgement means 271 (272), so as to obtain a reconstructed new background image 321 collectively.
  • the moving object extraction means 530 further comprises subtraction means 401, 402, and 403 for subtracting the R, G, and B images in the reconstructed background image obtained by the background image region reconstruction means 330 from the R, G, and B images 011, 012, and 013 in the color input image entered from the color camera 002 in units of pixels, and a binarization means 511. 512, and 513 for applying a binarization using a prescribed threshold to the subtraction results for the R, G, and B images, respectively.
  • the color image sequence of the input images are entered from the color camera 002, and the R, G, and B images (pixel values) are stored into the frame image memories 131, 132, and 133 as the image feature parameters. Then, as indicated in FIG. 7, three types of the pixel values for R, G, and B for each sub-region are given as a feature vector in a three-dimensional feature space for each sub-region at the n-dimensional vector generation means 240.
  • the similar feature vectors are also plotted during the period of time t 0 for each sub-region, so as to obtain the vector set during t 0 for each sub-region a k , such as the vector set 251 (252) for the sub-region a 1 (a 2 ).
  • the vector set 251 has the following feature. Namely, at the sub-region a 1 in which the moving object is absent, the feature vectors V1, V2, and V3 change only gradually over the period of time t 0 , and the distance W1 between the feature vectors V1 and V2 as well as the distance W2 between the feature vectors V2 and V3 have small values. On the other hand, at the sub-region a 2 in which the moving object is present, the feature vectors V1, V2, and V3 change largely over the period of time t 0 , and the distance W1 between the feature vectors V1 and V2 as well as the distance W2 between the feature vectors V2 and V3 have large values.
  • the statistical processing means 261 obtains the histogram 263 (264) of occurrences of feature values for the sub-region a 1 (a 2 ) as shown in FIG. 7 by taking the feature values on a horizontal axis and a frequency of occurrences on a vertical axis.
  • the histogram 263 for the sub-region a 1 has a distribution with a small variance ⁇
  • the histogram 264 for the sub-region a 2 has a distribution with a large variance ⁇ .
  • the illumination change judging condition judgment means 271 compares the variance ⁇ obtained by the respective statistical processing means 261 (262) with the predetermined variance ⁇ 0 indicating a threshold for separating a case due to the illumination change or a case due to the gradually changing background object from a case due to the passing of the moving object.
  • the change of the intensity value at the respective sub-region is judged as due to the illumination change or the gradually changing background object, in which case it is decided that the intensity value at the respective sub-region is to be updated.
  • the obtained variance ⁇ is greater than or equal to the predetermined variance ⁇ 0
  • the change of the intensity value at the respective sub-region is judged as due to the cause other than the illumination change or the gradually changing background object, such as the passing of the moving object, in which case it is decided that the intensity value at the respective sub-region is not to be updated.
  • the value update means 281 updates the intensity of the sub-region a1 (a 2 ) whenever necessary.
  • the reconstructed new background image 321 is obtained by the background image sub-region update means 204 in units of sub-regions.
  • the reconstructed new background image 321 contains R, G, and B images which are to be subjected to the subtraction processing and the binarization processing at the moving object extraction means 530 in conjunction with the R, G, B images in the color input image, so as to extract the moving object region 120.
  • a plurality (three) of image feature parameters are utilized in updating the background values and extracting the moving object, so that the judgment for a need to update the background value at each sub-region can be made accurately, and the moving object can be extracted stably.
  • the above described fourth embodiment it is possible to modify the above described fourth embodiment to use the other image feature parameters that can be extracted from color data obtained by a color camera such as any combination of the intensity, the hue, the saturation, the intensity gradient with respect to neighboring pixels, and other quantities expressed in the chromaticness scale system. It is also possible to use any combination of concentration data or temperature data obtained by an infrared camera, distance data obtained by a range sensor, and reflection intensity data obsensed by an ultrasonic sensor as the image feature parameters.
  • the sub-region may not necessarily be a single pixel as described above, and may be a block image formed by a plurality of pixels, in which case, the image feature parameter can be a value obtained by statistically processing a plurality of intensity values or other quantities qualifying as the image feature parameter, such as the mean value, the most frequent value, the maximum value, or the minimum value of the intensity values within the block image.
  • This fifth embodiment differs from the fourth embodiment described above only in that the background image sub-region update means 204 in the background image region reconstruction means 330 is replaced by the background image sub-region update means 205 in the background image region reconstruction means 300 so as to obtain the reconstructed new background image 322.
  • FIG. 8 shows a detailed functional configuration of the background image region reconstruction means 331 in this fifth embodiment.
  • the background image region reconstruction means 331 further comprises a plurality of background image sub-region update means 205 provided in correspondence to a plurality of sub-regions a k for updating the image feature parameter value of each sub-region a k
  • each background image sub-region update means 205 further includes the n-dimensional dimensional vector generation means 240 similar to that in the fourth embodiment described above, a discrete set calculation means 253 (254) for calculating the discrete set with respect to the imaging system characteristic curve L1 (L2) during the period of time to at the sub-region a 1 (a 2 ), a statistical processing means 265 (266) for statistically processing distances d (d1, d2, d3) of the the feature vectors V1, V2, and V3 from the characteristic curve L1 (L2), an illumination change judging condition judgement means 273 (274) for judging a need for updating the image feature parameter value at each sub-region due to an occurrence of the illumination change according to the statistical processing result at each sub-region, and a value
  • the characteristic curve of the three-dimensional vectors which change in conjunction with the illumination change is obtained in advance according to the background values b0, b1, etc. in each sub-region.
  • the change of the background value due to the illumination change can be assumed to occur along a straight line joining the current background value and the origin such that the characteristic curves can be approximated by the straight lines La and Lb shown in FIG. 9A.
  • the set of three feature vectors obtained by the discrete set calculation means 253 has the following feature. Namely, at the sub-region a 1 in which the moving object is absent, the feature vectors V1, V2, and V3 change only gradually from the characteristic curve L1 over the period of time t 0 , and the distances d1, d2, and d3 of the feature vectors V1, V2, and V3 from the characteristic curve L1 have small values.
  • the feature vectors V1, V2, and V3 change largely over the period of time t 0 , and the distances d1, d2, and d3 of the feature vectors V1, V2, and V3 from the characteristic curve L2 have large values.
  • the statistical processing means 265 (266) obtains the histogram 267 (268) of occurrences of feature values for the sub-region a 1 (a 2 ) as shown in FIG. 8 by taking the feature values on a horizontal axis and a frequency of occurrences on a vertical axis.
  • the histogram 267 for the sub-region a 1 has a distribution with a small variance ⁇
  • the histogram 268 for the sub-region a 2 has a distribution with a large variance ⁇ .
  • the illumination change judging condition judgment means 273 compares the variance ⁇ obtained by the respective statistical processing means 265 (266) with the predetermined variance ⁇ 0 indicating a threshold for separating a case due to the illumination change from a case due to the passing of the moving object.
  • the obtained variance ⁇ is less than the predetermined variance ⁇ 0 , the change of the intensity value at the respective sub-region is judged as due to the illumination change, in which case it is decided that the intensity value at the respective sub-region is to be updated.
  • the change of the intensity value at the respective sub-region is judged as due to the cause other than the illumination change, such as the passing of the moving object, in which case it is decided that the intensity value at the respective sub-region is not to be updated.
  • the value update means 283 updates the intensity of the sub-region a 1 (a 2 ) whenever necessary. In this manner, the reconstructed new background image 322 is obtained by the background image sub-region update means 205 in units of sub-regions.
  • the change of the feature vectors in each sub-region can be expressed in terms of the distance d from the characteristic curve as well as the motion vector u on the characteristic curve, such that a case of the abrupt illumination change (Y1, Y2, Y3) can be distinguished from a case of the gradual illumination change (Z1, Z2, Z3) according to the motion vectors u, while distinguishing a case of the passing of the moving object (X1, X2, X3) according to the distances d.
  • This sixth embodiment differs from the first embodiment described above only in that the background image sub-region update means 200 is replaced by the background image sub-region update means 201.
  • FIG. 10 shows a schematic configuration of the background image sub-region update means 201 in this sixth embodiment, while FIG. 11 shows a detailed functional configuration of the background image sub-region update means 201 in this sixth embodiment.
  • this background image sub-region update means 201 generally comprises an intensity change statistical processing means 215 for statistically processing the temporal change of the intensity at each sub-region during a prescribed period of time t 0 , and an illumination change judging condition judgement means 223 for judging a need for updating the image feature parameter value at each sub-region due to an occurrence of the illumination change according to a plurality of illumination change judging conditions on the statistically processed temporal change of the intensity at each sub-region, and a plurality of value update means 233 to 235 for updating the image feature parameter value at each sub-region according to the judgment result of the illumination change judging condition judgement means 223.
  • the intensity change statistical processing means 215 includes the statistical processing means 341, 342, and 345 for statistically processing the temporal change of the intensity at the respective sub-regions a 1 , a 2 , and as during a prescribed period of time t 0 , by using the histograms 351, 352, and 355 of occurrences of intensity values during the period of time t 0 at the respective sub-regions a 1 , a 2 , and a 5 to obtain the variances ⁇ 1 , ⁇ 2 , and ⁇ 5 in the respective sub-regions a 1 , a 2 , and a 5 , as well as the histograms 361, 362, and 365 of occurrences of temporal differential of intensity values during the period of time t 0 at the respective sub-regions a 1 , a 2 , and a 5 to obtain the maximum values m 1 , m 2 , and m 5 of the temporal differential of the intensity value during the period of time
  • the illumination change judging condition judgement means 223 judges a need for updating the image feature parameter value at each sub-region due to an occurrence of the illumination change according to the predetermined variance ⁇ 0 and the predetermined maximum value m 0 for the temporal differential of the intensity value.
  • the value update means I 233 is used for a case of the gradual illumination change
  • the value update means II 235 is used for a case of the abrupt illumination change.
  • the illumination change can include various manners of changes depending on the causes of the changes, so that in this sixth embodiment, different manners of updating the image feature parameter values are used for different types of the illumination changes, such that the background image can be reconstructed properly in accordance with a type of the illumination change which is judged by using a plurality of illumination change judging conditions.
  • the histograms 351 and 361 as shown in FIG. 11 will be obtained, so that the variance ⁇ and the maximum value m during the period of time t 0 is small as long as the period of time t 0 is as short as several seconds.
  • the variance ⁇ increases accordingly, and it becomes difficult to distinguish a case of the illumination change and a case of the cause other than the illumination change according to the value of the variance ⁇ alone, as can be seen in the histogram 355 for a case of the abrupt illumination change and the histogram 352 for a case of the cause other than the illumination change.
  • the maximum value m of the temporal differential df of the intensity value can be used, as this temporal differential df takes a small value for a case of the illumination change and a large value for a case of the passing of the moving object, as can be seen in the histogram 365 for a case of the abrupt illumination change and the histogram 362 for a case of the case other than the illumination change.
  • this temporal differential df takes particularly large values at a moment of the entry of the moving object into the image and at a moment of the exit of the moving object from the image.
  • the illumination change judging condition judgment means 223 compares the obtained variance ⁇ and maximum value m with the predetermined variance ⁇ 0 and maximum value m 0 , and judges it as a case of the gradual illumination change when the obtained variance ⁇ and maximum value m are less than the predetermined variance ⁇ 0 and maximum value m 0 , respectively, or as a case of the abrupt illumination change when the obtained variance ⁇ is greater than the predetermined variance ⁇ 0 but the obtained maximum value m is less than the predetermined maximum value m 0 , or else as a case of the passing of the moving object when the obtained variance ⁇ and maximum value m are greater than the predetermined variance ⁇ 0 and maximum value m 0 , respectively.
  • the background value at each sub-region is updated by the value update means I 233, and in a case of the abrupt illumination change, the background value at each sub-region is updated by the value update means II 235, whereas in a case of the passing of the moving object, the background value at each sub-region is not updated.
  • the value update means I 233 for a case of no illumination change or the gradual illumination change can replace the background value at each sub-region by the most frequency value of the intensity values during the period of time t 0
  • the value update means II 235 for a case of the abrupt illumination change can replace the background value at each sub-region by the mean value of the pixel values at each sub-region in several recent frames.
  • FIG. 12 shows a system configuration of a moving object extraction system in this seventh embodiment, which generally comprises: a camera 001 for entering an image sequence of the input images; an image feature parameter value temporal change storage means 103 including frame image memories for storing image feature parameter values for the sequentially entered input images; a background image region reconstruction means 300 for reconstructing the background image according to the temporal change of the stored image feature parameter values; and a moving object extraction means 501 for obtaining a moving object output 520 from the stored image 020 at a prescribed period of time t 1 earlier timing and the reconstructed background image 310.
  • the image 010 to be supplied to the moving object extraction means 500 and subjected to the subtraction processing at the subtraction means 400 with respect to the reconstructed background image 310 has been the latest input image 601 among the image sequence 600 entered from the camera 001.
  • the image 020 to be supplied to the moving object extraction means 501 and subjected to the subtraction processing at the subtraction means 401 with respect to the reconstructed background image 310 is set to be the input image at the prescribed period of time t 1 earlier timing among the image sequence 600 entered from the camera 001.
  • this seventh embodiment differs from the first embodiment described above in that the input image used in the subtraction processing is different. More specifically, the input image used in the subtraction processing at the subtraction means 400 in the first embodiment described above has been the image 010 entered from the camera 001 at the immediately before the subtraction processing is to be carried out, whereas the input image used in the subtraction processing at the subtraction means 401 in this seventh embodiment is the image 020 which had been entered from the camera 001 at the timing which is the prescribed period of time t 1 before the subtraction processing is to be carried out and which has been stored in the image feature parameter value temporal change storage means 103.
  • the images stored in the image feature parameter value temporal change storage means 103 are utilized not just in the subtraction processing, but also in the background updating processing at the background image region reconstruction means 300 as in the first embodiment described above.
  • the prescribed period of time t 0 used in the background updating processing is set to be longer than the prescribed period of time t 1 used in the subtraction processing.
  • the subtraction processing using the image 020 is going to be carried out with respect to the reconstructed background image 310 which has been reconstructed according to the images ranging from the image entered at t 0 -t 1 earlier timing (i.e., a past image with respect to the image 020) to the image entered at t 1 later timing (i.e., a future image with respect to the image 020) in this seventh embodiment.
  • FIGS. 14A and 14B show the temporal change 602 of the intensity value in a case of the gradually brightening illumination change for the first embodiment described above and this seventh embodiment, respectively, along with the subtraction processing target image intensity level 603 or 605 for a certain sub-region and the judging background image intensity level 604 or 606 for the same sub-region judging by using the images within a given period of time, where the horizontal axis represents time and the vertical axis represents the intensity value.
  • the images subjected to the subtraction processing are the image 020 at the prescribed period of time t 1 earlier timing than the current timing, and the reconstructed background image 310 which has been reconstructed according to the images ranging from the image entered at t 0 -t 1 earlier timing than the image 020 to the image entered at t 1 later timing than the image 020.
  • the value update means 239 updates the background value at each sub-region to a new background value which is equal to the mean value of the intensity values in all the images during a certain period of time t 2 centered around the timing t 1 of the image 020 as indicated in FIG.
  • the period of time t 2 used in this value updating processing is set to be shorter than the prescribed period of time t 1 used in the subtraction processing.
  • the background image intensity level 604 corresponding to the subtraction processing target image intensity level 603 is going to be judging by using only the images at earlier timings than the subtraction processing target image, and consequently there is a significant gap between the background image intensity level 604 and the subtraction processing target image intensity level 603.
  • this seventh embodiment there are images newer than the subtraction processing target image, and it can be recognized that the intensity values are changing in a single direction (from dark to bright) in a vicinity of the subtraction processing target image, so that by estimating the background image intensity level 606 corresponding to the subtraction processing target image intensity level 605 by using the images ranging from the past image to the future image with respect to the the subtraction processing target image, it is possible to obtain the background image intensity level 606 which is much closer to the subtraction processing target image intensity level 605.
  • FIG. 15 shows a system configuration of a moving object extraction system in this eighth embodiment, which differs from the first embodiment described above in that there is provided a threshold setting means 700 for setting the threshold to be used at the binarization means 512 in the moving object extraction means 502 according to the result of the statistical processing obtained by the intensity change statistical processing means 210 in the background image region reconstruction means 200.
  • the rest of this configuration FIG. 15 is identical to that of the first embodiment described above.
  • the threshold setting means 700 updates the threshold for each sub-region, according to the statistical information obtained for each sub-region by the intensity change statistical processing means 210, so as to adjust the threshold used in the binarization means 512 for binarizing the subtraction image for each sub-region appropriately.
  • the updating of the threshold for each sub-region according to the statistical information for each sub-region can be realized by updating the threshold for each sub-region according to the intensity value in the background image for each sub-region, as the intensity value in the sequentially updated background image for each sub-region in turn is determined in accordance with the statistical information for each sub-region.
  • FIGS. 16A, 16B, and 16C show exemplary settings of the threshold by the threshold setting means 700 according to the intensity value in the background image which in turn depends on the statistical information obtained for each sub-region by the intensity change statistical processing means 210, where the horizontal axis represents the background image intensity value and the vertical axis represents the absolute value of the difference obtained by the subtraction processing. More specifically, FIG. 16A shows a setting which is proportional to the background image intensity value, FIG. 16B shows a setting which is also proportional to the background image intensity value but the threshold has a certain lower limit such that the threshold has some non-zero value even for the darkest background image intensity value, and FIG. 16C shows a setting which is proportional to the background image intensity values darker than a certain background image intensity value but the threshold becomes constant for the background image intensity values brighter than that certain background image intensity value.
  • FIG. 17 shows a physical configuration suitable for the first embodiment of FIG. 3.
  • This configuration of FIG. 17 comprises an image input unit 171 functioning as the camera 001 of FIG. 3, an image storage unit 172 functioning as the image feature parameter value temporal change storage means 100 of FIG. 3, a plurality of background update units 173a, 173b, 173c, etc. functioning as the background image sub-region update means 200 of the background image region reconstruction means 300 of FIG. 3, an image subtraction calculation unit 174 functioning as the subtraction means 400 of FIG. 3, an image binarization calculation unit 175 functioning as the binarization means 510 of FIG. 3, and a moving object output unit 176 for outputting the moving object output 520 of FIG. 3.
  • the background update unit 173a further includes a pixel value statistical processing unit 1731 functioning as the intensity change statistical processing means 210 of FIG. 3, a statistical value judgement unit 1732 functioning as the illumination change judging condition judgment unit 220 of FIG. 3, a most frequent value calculation unit 1733 connected with a value update unit 1734 and a mean value calculation unit 1735 connected with a value update unit 1736 which function as the value update means 230 of FIG. 3.
  • Each of the other background update units 173b, 173c, etc. also has a similar internal configuration as the background update unit 173a.
  • FIG. 17 This physical configuration of FIG. 17 is also suitable for the third, fourth, fifth, and sixth embodiments described above.
  • FIG. 18 shows a physical configuration suitable for the second embodiment.
  • This configuration of FIG. 18 differs from the configuration of FIG. 17 in that there is provided a slit image acquisition unit 181 for acquiring the slit image, which is located between the image input unit 171 and the image storage unit 172.
  • FIG. 19 shows a physical configuration suitable for the seventh embodiment of FIG. 12.
  • This configuration of FIG. 19 differs from the configuration of FIG. 17 in that an image storage unit 191 is provided between the image input unit 171 and the image subtraction calculation unit 174, as well as between the image input unit 171 and the background update units 173, such that the images stored in the image storage unit 191 can be supplied to the image subtraction calculation unit 174 as well as the background update units 173.
  • FIG. 20 shows a physical configuration suitable for the eighth embodiment of FIG. 15.
  • This configuration of FIG. 20 differs from the configuration of FIG. 17 in that there is provided a value setting unit 177 for setting the threshold to be used in the image binarization calculation unit 175, which is located between the pixel value statistical processing unit 1731 of each background update unit 173 and the image binarization calculation unit 175.
  • FIG. 17 may be modified as shown in FIG. 21, where there is provided a background image storage unit 178 for storing the reconstructed background image, which is located between the background update units 173 and the image subtraction calculation unit 174, and each background update unit 173 incorporates a background value read out unit 1737 for reading out the background value from the statistical value judgement unit 1732 by bypassing the most frequent value calculation unit 1733 and the mean value calculation unit 1735, in response to a control from the background image storage unit 178.
  • the value update unit 1734 is shared among the most frequent value calculation unit 1733, the mean value calculation unit 1735, and the background value read out unit 1737.
  • FIG. 21 can be modified further as shown in FIG. 22, where the statistical value judgement unit 1732 makes the above noted additional judgement as to whether the updating of the background image itself is to be carried out or not, and when it is judged that the updating of the background image is not to be carried out, the most frequent value calculation unit 1733, the mean value calculation unit 1735, and the value update unit 1734 are bypassed.
  • the background image is treated as a set of sub-regions such as pixels, and whether the value change of the image feature parameter value in each sub-region is due to the illumination change or due to the passing of the moving object is judged by statistically processing the temporal change of the image feature parameter value.
  • the illumination change in the outdoor site is usually caused by the passing of the cloud in the sky, the change of the position of the sun, the change of the shadow, etc., and mainly comprised of the intensity value change.
  • the value change due to the illumination change is usually gradual compared with the change due to the passing of the moving object such as a human being or an automobile, so that by measuring the statistical quantity such as the variance according to the image feature parameter values within a prescribed period of time, it is possible to detect the occurrence of the value change due to the illumination change at each sub-region.
  • the illumination change is more or less present always, so that by carrying out the above processing continuously, it is possible to update the background image regularly in units of a prescribed period of time.
  • a region for applying this updating processing can be expanded to any desired region within the image. Consequently, even if the illumination change is not uniform over the entire image, it is possible to update the background image appropriately.
  • the image feature parameter value changes at a region where the moving object is present, so that this moving object region can be stably detected by using appropriate thresholding.
  • the extreme value or the mean value of the histogram over a prescribed period of time corresponding to the value at each sub-region is used, so that the statistically most likely background value at that moment is used in updating, and therefore-the subsequent subtraction processing and binarization processing can be stabilized.
  • the sub-region is set to be a pixel in the image
  • the desired region for applying the background updating processing is set to be a single line (slit) in the image, so that the space-time image formed by that line and the time axis can be produced easily at high speed.
  • the binarization processing can also be carried out at high speed.
  • the statistical quantity such as the variance in the histogram of the values in each sub-region is relatively compared with the statistical quantity such as the variance in the histograms of the values in other sub-regions in a vicinity of that sub-region.
  • the change of the background image due to the illumination change is gradual not just in the time direction but also in the space direction as well. Consequently, by relatively comparing the change in one sub-region with the changes in the surrounding sub-regions, it is possible to carry out the updating processing by judging the change as that due to the illumination change when the change is judged to be uniform over an appropriate region on the image,
  • the presence or absence of the illumination change is judging by the statistical processing of the value changes in n types of the image feature parameter values, so that the background updating processing can be carried out more stably.
  • the change in the background can be analyzed more minutely by using a plurality of feature quantities, so that not just a case of the image change due to the illumination change, but also a case of the gradually changing background can also be detected and the appropriate updating can be carried out.
  • the histogram of the distances of each feature vector between frames is obtained, and the presence or absence of the illumination change is detected from the statistical quantity such as the variance of this histogram.
  • the vector distances between frames are relatively small, whereas even the distance between two vectors in one frame becomes large in a case of the passing of the moving object. Consequently, a case of the illumination change can be detected from this histogram, and the gradual change of the background can also be detected. from this histogram.
  • the characteristic curve of the n-dimensional feature vectors which change in conjunction with the illumination change is determined in advance.
  • the change due to the illumination change is mainly the intensity value change, and in a case of the intensity value change, the feature vectors move substantially along the predetermined characteristic curve.
  • the feature vectors move substantially along the predetermined characteristic curve.
  • the histogram of distances of the feature vectors from the characteristic curve is used.
  • the feature vectors do not deviate largely from the the characteristic curve in a case of the illumination change in general, so that the mean value of the distances is going to be small, and the variance is also not going to be very large.
  • the distances are going to be large and the variance is also going to be large.
  • these two cases can be distinguished according to these differences.
  • by using the mean value and the variance in combination it is possible to improve the reliability of the judgment.
  • the characteristic curve itself has changed as the background has changed for some reason, it is possible to generate the characteristic curve for a case of the illumination change in correspondence to the new background by estimating the change of the background.
  • a plurality of illumination change judging conditions are used, so that the background updating processing can be carried out according to the manner of the illumination change, and it is possible to carry out the moving object extraction accounting for various types of the illumination changes.
  • the subtraction processing is carried out for the updated background image and the stored past input image, and this past input image is one of the images within the prescribed period of time t 0 used in the updating processing, so that the updated background image can be more stable with respect to the illumination change and the change of the background itself in this subtraction processing with respect to the stored past input image, compared with a case of the subtraction processing with respect to the latest input image.
  • the background image is updated by using the mean of the images centered around the subtraction processing target image, within a relatively short period of time t 2 , so that even in a case of the abrupt illumination change, the background image can be updated stably, and in addition, the subtraction processing is carried out between the updated background image and the image for which the background can be expected to be closest, so that the stable subtraction processing can be realized.
  • the threshold used in the binarization processing subsequent to the subtraction processing is varied according to the illumination condition at each sub-region in the image, so that the binarization processing can be carried out stably with respect to the change of the intensity value difference between the background image and the image to be extracted caused by the change in the brightness due to the illumination condition.
  • the background image can be updated accordingly at that point. Consequently, it is possible to realize the moving object extraction based on background subtraction which is quite robust against the illumination changes.
  • the present invention can be utilized in a wide range of fields requiring the moving object extraction. In particular, it is suitable for the extraction of the human being and the automobile. As the present invention can stably detect the passing of the moving object, it is effective in the applications such as an intruder monitoring at a manufacturing plant facility and a safety monitoring in a traffic facility such as a platform at a station.
  • the present invention can be utilized for counting of passing persons, in which case it is possible to extract the passing person regardless of the weather and the time of the measurement location, without being influenced by the noises originating from the imaging system such as a camera or the image transfer system, so that the accuracy in counting can be improved considerably.
  • the present invention almost never lose a sight of a target moving object, so that it can be utilized effectively for the detection of the trajectory of the moving object by consecutively carrying out the moving object extraction over the entire image field.

Landscapes

  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Human Computer Interaction (AREA)
  • Image Analysis (AREA)
  • Closed-Circuit Television Systems (AREA)
  • Image Processing (AREA)
US08/401,972 1994-03-09 1995-03-09 Method and apparatus for moving object extraction based on background subtraction Expired - Fee Related US5748775A (en)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
JP6-037438 1994-03-09
JP3743894 1994-03-09
JP2922095A JP3123587B2 (ja) 1994-03-09 1995-02-17 背景差分による動物体領域抽出方法
JP7-029220 1995-02-17

Publications (1)

Publication Number Publication Date
US5748775A true US5748775A (en) 1998-05-05

Family

ID=26367390

Family Applications (1)

Application Number Title Priority Date Filing Date
US08/401,972 Expired - Fee Related US5748775A (en) 1994-03-09 1995-03-09 Method and apparatus for moving object extraction based on background subtraction

Country Status (4)

Country Link
US (1) US5748775A (de)
EP (1) EP0671706B1 (de)
JP (1) JP3123587B2 (de)
DE (1) DE69510252T2 (de)

Cited By (139)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5991428A (en) * 1996-09-12 1999-11-23 Kabushiki Kaisha Toshiba Moving object detection apparatus and method
US6075535A (en) * 1998-06-26 2000-06-13 Hewlett-Packard Company Method and apparatus for visualizing the tile access frequencies for tiled, multi-resolution images
WO2000073996A1 (en) * 1999-05-28 2000-12-07 Glebe Systems Pty Ltd Method and apparatus for tracking a moving object
US6184858B1 (en) * 1998-02-06 2001-02-06 Compaq Computer Corporation Technique for updating a background image
US20010004400A1 (en) * 1999-12-20 2001-06-21 Takahiro Aoki Method and apparatus for detecting moving object
US20010005219A1 (en) * 1999-12-27 2001-06-28 Hideaki Matsuo Human tracking device, human tracking method and recording medium recording program thereof
US6259827B1 (en) * 1996-03-21 2001-07-10 Cognex Corporation Machine vision methods for enhancing the contrast between an object and its background using multiple on-axis images
US6333993B1 (en) * 1997-10-03 2001-12-25 Nec Corporation Method and device of object detectable and background removal, and storage media for storing program thereof
US20020030739A1 (en) * 1995-02-17 2002-03-14 Shigeki Nagaya Moving object detection apparatus
WO2002031751A1 (en) 2000-10-10 2002-04-18 Lockheed Martin Balanced object tracker in an image sequence
US20020051579A1 (en) * 2000-09-21 2002-05-02 Jacques Dugue Method and device for characterizing or controlling zones of temporal fluctuations of a scene
US20020057343A1 (en) * 2000-06-30 2002-05-16 Ronk Lawrence J. Image object ranking
US20020097893A1 (en) * 2001-01-20 2002-07-25 Lee Seong-Deok Apparatus and method for generating object-labeled image in video sequence
US20020136449A1 (en) * 2001-01-20 2002-09-26 Samsung Electronics Co., Ltd. Apparatus and method for extracting object based on feature matching between segmented regions in images
US20020168009A1 (en) * 2001-03-22 2002-11-14 Tatsumi Sakaguchi Moving picture encoder, moving picture encoding method, moving picture encoding program used therewith, and storage medium storing the same
US20020186881A1 (en) * 2001-05-31 2002-12-12 Baoxin Li Image background replacement method
US20030004652A1 (en) * 2001-05-15 2003-01-02 Daniela Brunner Systems and methods for monitoring behavior informatics
US6546115B1 (en) * 1998-09-10 2003-04-08 Hitachi Denshi Kabushiki Kaisha Method of updating reference background image, method of detecting entering objects and system for detecting entering objects using the methods
US20030118233A1 (en) * 2001-11-20 2003-06-26 Andreas Olsson Method and device for identifying objects in digital images
US20030161504A1 (en) * 2002-02-27 2003-08-28 Nec Corporation Image recognition system and recognition method thereof, and program
US6681058B1 (en) * 1999-04-15 2004-01-20 Sarnoff Corporation Method and apparatus for estimating feature values in a region of a sequence of images
US20040013300A1 (en) * 2002-07-17 2004-01-22 Lee Harry C. Algorithm selector
US20040042674A1 (en) * 2002-09-02 2004-03-04 Canon Kabushiki Kaisha Image processing apparatus and method
US20040046896A1 (en) * 1995-05-26 2004-03-11 Canon Kabushiki Kaisha Image processing apparatus and method
US6731799B1 (en) * 2000-06-01 2004-05-04 University Of Washington Object segmentation with background extraction and moving boundary techniques
US20040105856A1 (en) * 2002-12-02 2004-06-03 Robin Thurmond Use of histamine H4 receptor antagonist for the treatment of inflammatory responses
AU774180B2 (en) * 1999-05-28 2004-06-17 It Brokerage Services Pty Limited Method and apparatus for tracking a moving object
US20040141633A1 (en) * 2003-01-21 2004-07-22 Minolta Co., Ltd. Intruding object detection device using background difference method
US20040141635A1 (en) * 2000-11-24 2004-07-22 Yiqing Liang Unified system and method for animal behavior characterization from top view using video analysis
US6798909B2 (en) * 1999-12-27 2004-09-28 Hitachi, Ltd. Surveillance apparatus and recording medium recorded surveillance program
US20050108775A1 (en) * 2003-11-05 2005-05-19 Nice System Ltd Apparatus and method for event-driven content analysis
US6901165B1 (en) * 1998-09-30 2005-05-31 Siemens Aktiengesellschaft Method of automatically triggering pattern recognition procedures
US6950130B1 (en) 1999-01-05 2005-09-27 Sharp Laboratories Of America, Inc. Method of image background replacement
US20050254728A1 (en) * 2004-05-13 2005-11-17 Zhuo-Ya Wang Automatic cutting method for digital images
US20060045185A1 (en) * 2004-08-31 2006-03-02 Ramot At Tel-Aviv University Ltd. Apparatus and methods for the detection of abnormal motion in a video stream
US7013035B2 (en) * 1998-09-25 2006-03-14 Canon Kabushiki Kaisha Image processing method for setting an extraction area, and apparatus and recording medium
US20060072101A1 (en) * 2004-10-05 2006-04-06 Young-Kug Park System and method for measuring tip velocity of sprayed fuel
US20060110049A1 (en) * 2000-11-24 2006-05-25 Clever Sys, Inc. System and method for animal seizure detection and classification using video analysis
US7058221B1 (en) * 2000-07-07 2006-06-06 Tani Electronics Industry Co., Ltd. Method of recognizing object based on pattern matching and medium for recording computer program having same
US20060126956A1 (en) * 2004-12-14 2006-06-15 Lg Electronics Inc. Method of coding and decoding image
US20060245618A1 (en) * 2005-04-29 2006-11-02 Honeywell International Inc. Motion detection in a video stream
US20060284895A1 (en) * 2005-06-15 2006-12-21 Marcu Gabriel G Dynamic gamma correction
US20070071296A1 (en) * 2005-09-28 2007-03-29 Ryosuke Nonaka Radiographic image processing apparatus for processing radiographic image taken with radiation, method of radiographic image processing, and computer program product therefor
US20070081094A1 (en) * 2005-10-11 2007-04-12 Jean-Pierre Ciudad Image capture
US20070081740A1 (en) * 2005-10-11 2007-04-12 Jean-Pierre Ciudad Image capture and manipulation
US20070230744A1 (en) * 2006-03-29 2007-10-04 Mark Dronge Security alarm system
US20070274402A1 (en) * 2006-05-23 2007-11-29 Honeywell International Inc. Application of short term and long term background scene dynamics in motion detection
US20070291135A1 (en) * 2006-06-20 2007-12-20 Baer Richard L Motion characterization sensor
US20080034292A1 (en) * 2006-08-04 2008-02-07 Apple Computer, Inc. Framework for graphics animation and compositing operations
US20080030504A1 (en) * 2006-08-04 2008-02-07 Apple Inc. Framework for Graphics Animation and Compositing Operations
US20080120626A1 (en) * 2006-11-17 2008-05-22 Peter Graffagnino Methods and apparatuses for providing a hardware accelerated web engine
US7391444B1 (en) * 1999-07-23 2008-06-24 Sharp Kabushiki Kaisha Image pickup apparatus capable of selecting output according to time measured by timer
US20080199095A1 (en) * 2007-02-20 2008-08-21 Microsoft Corporation Pixel Extraction And Replacement
US20080303949A1 (en) * 2007-06-08 2008-12-11 Apple Inc. Manipulating video streams
US20080307307A1 (en) * 2007-06-08 2008-12-11 Jean-Pierre Ciudad Image capture and manipulation
US20080310677A1 (en) * 2007-06-18 2008-12-18 Weismuller Thomas P Object detection system and method incorporating background clutter removal
US20090016600A1 (en) * 2007-07-11 2009-01-15 John Eric Eaton Cognitive model for a machine-learning engine in a video analysis system
US20090044136A1 (en) * 2007-08-06 2009-02-12 Apple Inc. Background removal tool for a presentation application
WO2009026966A1 (en) * 2007-08-31 2009-03-05 Siemens Building Technologies Fire & Security Products Gmbh & Co.Ohg Method of estimating illumination change of images for object detection
US20090066802A1 (en) * 2007-09-06 2009-03-12 Suguru Itagaki Image processing device and method
US20090087085A1 (en) * 2007-09-27 2009-04-02 John Eric Eaton Tracker component for behavioral recognition system
US20090087027A1 (en) * 2007-09-27 2009-04-02 John Eric Eaton Estimator identifier component for behavioral recognition system
US20090087024A1 (en) * 2007-09-27 2009-04-02 John Eric Eaton Context processor for video analysis system
US20090190799A1 (en) * 2006-09-20 2009-07-30 Forschungszentrum Karlsruhe Gmbh Method for characterizing the exhaust gas burn-off quality in combustion systems
US7590261B1 (en) 2003-07-31 2009-09-15 Videomining Corporation Method and system for event detection by analysis of linear feature occlusion
US20090279779A1 (en) * 1997-12-23 2009-11-12 Intel Corporation Image Selection Based on Image Content
US20090279738A1 (en) * 2008-05-08 2009-11-12 Denso Corporation Apparatus for image recognition
US20090324011A1 (en) * 2008-06-25 2009-12-31 Lin Daw-Tung Method of detecting moving object
US20100111359A1 (en) * 2008-10-30 2010-05-06 Clever Sys, Inc. System and method for stereo-view multiple animal behavior characterization
CN1766929B (zh) * 2004-10-29 2010-05-12 中国科学院计算技术研究所 一种基于三维数据库的运动对象运动重构方法
US20100150471A1 (en) * 2008-12-16 2010-06-17 Wesley Kenneth Cobb Hierarchical sudden illumination change detection using radiance consistency within a spatial neighborhood
US20100157049A1 (en) * 2005-04-03 2010-06-24 Igal Dvir Apparatus And Methods For The Semi-Automatic Tracking And Examining Of An Object Or An Event In A Monitored Site
US20100182433A1 (en) * 2007-10-17 2010-07-22 Hitachi Kokusai Electric, Inc. Object detection system
US20100208986A1 (en) * 2009-02-18 2010-08-19 Wesley Kenneth Cobb Adaptive update of background pixel thresholds using sudden illumination change detection
US20100260376A1 (en) * 2009-04-14 2010-10-14 Wesley Kenneth Cobb Mapper component for multiple art networks in a video analysis system
US7852353B1 (en) 2005-03-31 2010-12-14 Apple Inc. Encoding a transparency (alpha) channel in a video bitstream
US7865834B1 (en) 2004-06-25 2011-01-04 Apple Inc. Multi-way video conferencing user interface
US20110043625A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Scene preset identification using quadtree decomposition analysis
US20110044533A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Visualizing and updating learned event maps in surveillance systems
US20110044499A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Inter-trajectory anomaly detection using adaptive voting experts in a video surveillance system
US20110044498A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Visualizing and updating learned trajectories in video surveillance systems
US20110043536A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Visualizing and updating sequences and segments in a video surveillance system
US20110043689A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Field-of-view change detection
US20110044536A1 (en) * 2008-09-11 2011-02-24 Wesley Kenneth Cobb Pixel-level based micro-feature extraction
US20110044492A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Adaptive voting experts for incremental segmentation of sequences with prediction in a video surveillance system
US20110044537A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Background model for complex and dynamic scenes
US20110043626A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Intra-trajectory anomaly detection using adaptive voting experts in a video surveillance system
US20110052068A1 (en) * 2009-08-31 2011-03-03 Wesley Kenneth Cobb Identifying anomalous object types during classification
US20110052003A1 (en) * 2009-09-01 2011-03-03 Wesley Kenneth Cobb Foreground object detection in a video surveillance system
US20110052000A1 (en) * 2009-08-31 2011-03-03 Wesley Kenneth Cobb Detecting anomalous trajectories in a video surveillance system
US20110051992A1 (en) * 2009-08-31 2011-03-03 Wesley Kenneth Cobb Unsupervised learning of temporal anomalies for a video surveillance system
US20110052002A1 (en) * 2009-09-01 2011-03-03 Wesley Kenneth Cobb Foreground object tracking
US20110052067A1 (en) * 2009-08-31 2011-03-03 Wesley Kenneth Cobb Clustering nodes in a self-organizing map using an adaptive resonance theory network
US20110050896A1 (en) * 2009-08-31 2011-03-03 Wesley Kenneth Cobb Visualizing and updating long-term memory percepts in a video surveillance system
US20110050897A1 (en) * 2009-08-31 2011-03-03 Wesley Kenneth Cobb Visualizing and updating classifications in a video surveillance system
US7903141B1 (en) 2005-02-15 2011-03-08 Videomining Corporation Method and system for event detection by multi-scale image invariant analysis
US20110064268A1 (en) * 2009-09-17 2011-03-17 Wesley Kenneth Cobb Video surveillance system configured to analyze complex behaviors using alternating layers of clustering and sequencing
US20110064267A1 (en) * 2009-09-17 2011-03-17 Wesley Kenneth Cobb Classifier anomalies for observed behaviors in a video surveillance system
US7925978B1 (en) * 2006-07-20 2011-04-12 Adobe Systems Incorporated Capturing frames from an external source
US20110243451A1 (en) * 2010-03-30 2011-10-06 Hideki Oyaizu Image processing apparatus and method, and program
US20120106788A1 (en) * 2010-10-29 2012-05-03 Keyence Corporation Image Measuring Device, Image Measuring Method, And Computer Program
US20120169937A1 (en) * 2011-01-05 2012-07-05 Canon Kabushiki Kaisha Image processing apparatus and image processing method
US20120177121A1 (en) * 2009-09-04 2012-07-12 Stmicroelectronics Pvt. Ltd. Advance video coding with perceptual quality scalability for regions of interest
US8582815B2 (en) 2011-02-23 2013-11-12 Denso Corporation Moving object detection apparatus
US8620028B2 (en) 2007-02-08 2013-12-31 Behavioral Recognition Systems, Inc. Behavioral recognition system
US20140133753A1 (en) * 2012-11-09 2014-05-15 Ge Aviation Systems Llc Spectral scene simplification through background subtraction
US20150003743A1 (en) * 2011-12-19 2015-01-01 Panasonic Corporation Object detection device and object detection method
US9104918B2 (en) 2012-08-20 2015-08-11 Behavioral Recognition Systems, Inc. Method and system for detecting sea-surface oil
US9113143B2 (en) 2012-06-29 2015-08-18 Behavioral Recognition Systems, Inc. Detecting and responding to an out-of-focus camera in a video analytics system
US9111353B2 (en) 2012-06-29 2015-08-18 Behavioral Recognition Systems, Inc. Adaptive illuminance filter in a video analysis system
US9111148B2 (en) 2012-06-29 2015-08-18 Behavioral Recognition Systems, Inc. Unsupervised learning of feature anomalies for a video surveillance system
US20150254513A1 (en) * 2014-03-10 2015-09-10 Mitsubishi Electric Research Laboratories, Inc. Method for Extracting Low-Rank Descriptors from Images and Videos for Querying, Classification, and Object Detection
US9208675B2 (en) 2012-03-15 2015-12-08 Behavioral Recognition Systems, Inc. Loitering detection in a video surveillance system
US9232140B2 (en) 2012-11-12 2016-01-05 Behavioral Recognition Systems, Inc. Image stabilization techniques for video surveillance systems
US9317908B2 (en) 2012-06-29 2016-04-19 Behavioral Recognition System, Inc. Automatic gain control filter in a video analysis system
US20160117842A1 (en) * 2014-10-27 2016-04-28 Playsight Enteractive Ltd. Object extraction from video images
US9349054B1 (en) 2014-10-29 2016-05-24 Behavioral Recognition Systems, Inc. Foreground detector for video analytics system
US9460522B2 (en) 2014-10-29 2016-10-04 Behavioral Recognition Systems, Inc. Incremental update for background model thresholds
US9471844B2 (en) 2014-10-29 2016-10-18 Behavioral Recognition Systems, Inc. Dynamic absorption window for foreground background detector
US9507768B2 (en) 2013-08-09 2016-11-29 Behavioral Recognition Systems, Inc. Cognitive information security using a behavioral recognition system
US20170154427A1 (en) * 2015-11-30 2017-06-01 Raytheon Company System and Method for Generating a Background Reference Image from a Series of Images to Facilitate Moving Object Identification
US9723271B2 (en) 2012-06-29 2017-08-01 Omni Ai, Inc. Anomalous stationary object detection and reporting
US20170280154A1 (en) * 2014-09-25 2017-09-28 Sony Semiconductor Solutions Corporation Signal processing apparatus, imaging apparatus, and signal processing method
US9813731B2 (en) 2009-09-04 2017-11-07 Stmicroelectonics International N.V. System and method for object based parametric video coding
US20170337431A1 (en) * 2016-05-18 2017-11-23 Canon Kabushiki Kaisha Image processing apparatus and method and monitoring system
US9911043B2 (en) 2012-06-29 2018-03-06 Omni Ai, Inc. Anomalous object interaction detection and reporting
US20180296183A1 (en) * 2014-11-04 2018-10-18 Vib Vzw Method and apparatus for ultrasound imaging of brain activity
US10178396B2 (en) * 2009-09-04 2019-01-08 Stmicroelectronics International N.V. Object tracking
DE102017011604A1 (de) 2017-12-14 2019-06-19 Kuka Deutschland Gmbh Verfahren und System zum Erstellen eines Modells
US10409909B2 (en) 2014-12-12 2019-09-10 Omni Ai, Inc. Lexical analyzer for a neuro-linguistic behavior recognition system
US10410371B2 (en) 2017-12-21 2019-09-10 The Boeing Company Cluttered background removal from imagery for object detection
US10409910B2 (en) 2014-12-12 2019-09-10 Omni Ai, Inc. Perceptual associative memory for a neuro-linguistic behavior recognition system
US20190370977A1 (en) * 2017-01-30 2019-12-05 Nec Corporation Moving object detection apparatus, moving object detection method and program
US10643076B2 (en) 2016-07-01 2020-05-05 International Business Machines Corporation Counterfeit detection
WO2021001323A1 (en) 2019-07-01 2021-01-07 Thales Dis France Sa Method to generate a slap/fingers foreground mask
US11127141B2 (en) * 2018-11-27 2021-09-21 Canon Kabushiki Kaisha Image processing apparatus, image processing method, and a non-transitory computer readable storage medium
US11276186B2 (en) * 2018-06-14 2022-03-15 Canon Kabushiki Kaisha Image processing apparatus, image capturing apparatus, image processing method, and non-transitory computer-readable storage medium
US11288820B2 (en) 2018-06-09 2022-03-29 Lot Spot Inc. System and method for transforming video data into directional object count
US11461903B2 (en) 2018-05-24 2022-10-04 Nippon Telegraph And Telephone Corporation Video processing device, video processing method, and video processing program

Families Citing this family (51)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3679512B2 (ja) 1996-07-05 2005-08-03 キヤノン株式会社 画像抽出装置および方法
US5953055A (en) * 1996-08-08 1999-09-14 Ncr Corporation System and method for detecting and analyzing a queue
GB9617592D0 (en) * 1996-08-22 1996-10-02 Footfall Limited Video imaging systems
US6453069B1 (en) 1996-11-20 2002-09-17 Canon Kabushiki Kaisha Method of extracting image from input image using reference image
ATE402457T1 (de) * 1999-12-23 2008-08-15 Secuman B V Verfahren, vorrichtung und rechnerprogramm zur überwachung eines gebiets
US8564661B2 (en) 2000-10-24 2013-10-22 Objectvideo, Inc. Video analytic rule detection system and method
US9892606B2 (en) 2001-11-15 2018-02-13 Avigilon Fortress Corporation Video surveillance system employing video primitives
US8711217B2 (en) 2000-10-24 2014-04-29 Objectvideo, Inc. Video surveillance system employing video primitives
US7424175B2 (en) 2001-03-23 2008-09-09 Objectvideo, Inc. Video segmentation using statistical pixel modeling
JP4631199B2 (ja) * 2001-04-13 2011-02-16 ソニー株式会社 画像処理装置および画像処理方法、記録媒体、並びにプログラム
RU2308761C2 (ru) 2001-09-07 2007-10-20 Интегрэф Софтвеа Текнолоджис Кампэни Система для обнаружения первого объекта, скрытого вторым объектом, способ визуального отображения первого объекта и способ представления на экране дисплея данных первого объекта
AUPR899401A0 (en) * 2001-11-21 2001-12-13 Cea Technologies Pty Limited Method and apparatus for non-motion detection
JP4233272B2 (ja) * 2002-05-23 2009-03-04 株式会社エイブイプランニングセンター 物体計数方法及び物体計数装置
US7190809B2 (en) 2002-06-28 2007-03-13 Koninklijke Philips Electronics N.V. Enhanced background model employing object classification for improved background-foreground segmentation
JP4555986B2 (ja) * 2004-07-09 2010-10-06 財団法人生産技術研究奨励会 背景画像生成方法及び装置
WO2006011593A1 (ja) 2004-07-30 2006-02-02 Matsushita Electric Works, Ltd. 個体検出器及び共入り検出装置
WO2006038073A2 (en) * 2004-10-04 2006-04-13 Gavin Hough Image processing
JP4637564B2 (ja) * 2004-12-22 2011-02-23 株式会社リコー 状態検知装置、状態検知方法、プログラムおよび記録媒体
JP4641477B2 (ja) * 2005-09-16 2011-03-02 日本電信電話株式会社 映像変化抽出方法、映像変化抽出装置、及び映像変化抽出プログラム
JP2007148835A (ja) 2005-11-28 2007-06-14 Fujitsu Ten Ltd 物体判別装置、報知制御装置、物体判別方法および物体判別プログラム
WO2007077672A1 (ja) * 2005-12-28 2007-07-12 Olympus Medical Systems Corp. 画像処理装置および当該画像処理装置における画像処理方法
JP4852355B2 (ja) * 2006-06-26 2012-01-11 パナソニック株式会社 放置物検出装置及び放置物検出方法
JP4788525B2 (ja) 2006-08-30 2011-10-05 日本電気株式会社 物体識別パラメータ学習システム、物体識別パラメータ学習方法および物体識別パラメータ学習用プログラム
JP4757173B2 (ja) 2006-11-17 2011-08-24 キヤノン株式会社 撮像装置及びその制御方法及びプログラム
US8498695B2 (en) 2006-12-22 2013-07-30 Novadaq Technologies Inc. Imaging system with a single color image sensor for simultaneous fluorescence and color video endoscopy
US8345742B2 (en) * 2007-06-04 2013-01-01 Enswers Co., Ltd. Method of processing moving picture and apparatus thereof
RU2510235C2 (ru) 2008-03-18 2014-03-27 Новадак Текнолоджиз Инк. Система визуализации для получения комбинированного изображения из полноцветного изображения в отраженном свете и изображение в ближней инфракрасной области
EP2302564A1 (de) * 2009-09-23 2011-03-30 Iee International Electronics & Engineering S.A. Dynamische Echtzeitreferenzbilderzeugung für ein Entfernungsbildgebungssystem
JP2011198270A (ja) * 2010-03-23 2011-10-06 Denso It Laboratory Inc 対象認識装置及びそれを用いた制御装置、並びに対象認識方法
JP5653174B2 (ja) * 2010-10-29 2015-01-14 株式会社キーエンス 動画追尾装置、動画追尾方法および動画追尾プログラム
JP5971678B2 (ja) * 2011-11-01 2016-08-17 株式会社東芝 情報出力装置、検知装置、プログラム及び情報出力方法
US9530221B2 (en) * 2012-01-06 2016-12-27 Pelco, Inc. Context aware moving object detection
JP5929379B2 (ja) * 2012-03-19 2016-06-08 富士通株式会社 画像処理装置、画像処理方法及びプログラム
JP5897950B2 (ja) * 2012-03-27 2016-04-06 セコム株式会社 画像監視装置
JP5903308B2 (ja) * 2012-03-27 2016-04-13 セコム株式会社 画像監視装置
EP2733933A1 (de) * 2012-09-19 2014-05-21 Thomson Licensing Verfahren und Vorrichtung zur Kompensierung von Beleuchtungsvariationen in einer Sequenz aus Bildern
CN103679196A (zh) * 2013-12-05 2014-03-26 河海大学 视频监控中的人车自动分类方法
GB2525587A (en) * 2014-04-14 2015-11-04 Quantum Vision Technologies Ltd Monocular camera cognitive imaging system for a vehicle
JP6415178B2 (ja) * 2014-08-19 2018-10-31 キヤノン株式会社 印刷装置及びデータの更新方法
JP6602009B2 (ja) 2014-12-16 2019-11-06 キヤノン株式会社 画像処理装置、画像処理方法、及びプログラム
JP6058720B2 (ja) * 2015-03-13 2017-01-11 株式会社東芝 情報出力装置、検知装置、プログラム及び情報出力方法
WO2017077261A1 (en) 2015-11-05 2017-05-11 Quantum Vision Technologies Ltd A monocular camera cognitive imaging system for a vehicle
CN113648067A (zh) 2015-11-13 2021-11-16 史赛克欧洲运营有限公司 用于目标的照明和成像的系统和方法
EP4155716A1 (de) 2016-01-26 2023-03-29 Stryker European Operations Limited Bildsensoranordnung
JP6412032B2 (ja) * 2016-01-29 2018-10-24 セコム株式会社 空間認識装置
USD916294S1 (en) 2016-04-28 2021-04-13 Stryker European Operations Limited Illumination and imaging device
WO2017214730A1 (en) * 2016-06-14 2017-12-21 Novadaq Technologies Inc. Methods and systems for adaptive imaging for low light signal enhancement in medical visualization
EP3580609B1 (de) 2017-02-10 2023-05-24 Stryker European Operations Limited Handhaltbare offenfeld-fluoreszenzbildgebungssysteme und verfahren
JP2019121069A (ja) * 2017-12-28 2019-07-22 キヤノン株式会社 画像処理装置、画像処理方法、及びプログラム
WO2019146184A1 (ja) 2018-01-29 2019-08-01 日本電気株式会社 処理装置、処理方法及びプログラム
JP7062611B2 (ja) 2019-03-27 2022-05-06 Kddi株式会社 領域抽出装置及びプログラム

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4075604A (en) * 1976-02-23 1978-02-21 Tasco S.P.A. Method and apparatus for real time image recognition
US4741046A (en) * 1984-07-27 1988-04-26 Konishiroku Photo Industry Co., Ltd. Method of discriminating pictures
JPS63194477A (ja) * 1987-02-07 1988-08-11 Nippon Telegr & Teleph Corp <Ntt> 背景画像抽出方法
US4807163A (en) * 1985-07-30 1989-02-21 Gibbons Robert D Method and apparatus for digital analysis of multiple component visible fields
US4847677A (en) * 1988-04-27 1989-07-11 Universal Video Communications Corp. Video telecommunication system and method for compressing and decompressing digital color video data
US5027413A (en) * 1988-06-17 1991-06-25 U.S. Philips Corp. Target detection systems
WO1991012584A2 (de) * 1990-02-09 1991-08-22 Siemens Aktiengesellschaft Verfahren zur bestimmung der momentanen lage und form von bewegten objekten und zu deren anzeige als binärbild
WO1992003801A1 (en) * 1990-08-24 1992-03-05 The Board Of Regents Of The University Of Oklahoma Method and apparatus for detecting and quantifying motion of a body part
US5150432A (en) * 1990-03-26 1992-09-22 Kabushiki Kaisha Toshiba Apparatus for encoding/decoding video signals to improve quality of a specific region
JPH05225341A (ja) * 1992-02-13 1993-09-03 Matsushita Electric Ind Co Ltd 移動物体検出装置
JPH0622318A (ja) * 1992-05-18 1994-01-28 Mitsubishi Electric Corp 移動物体抽出装置
JPH0652311A (ja) * 1992-07-31 1994-02-25 Kubota Corp 画像処理方法

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4075604A (en) * 1976-02-23 1978-02-21 Tasco S.P.A. Method and apparatus for real time image recognition
US4741046A (en) * 1984-07-27 1988-04-26 Konishiroku Photo Industry Co., Ltd. Method of discriminating pictures
US4807163A (en) * 1985-07-30 1989-02-21 Gibbons Robert D Method and apparatus for digital analysis of multiple component visible fields
JPS63194477A (ja) * 1987-02-07 1988-08-11 Nippon Telegr & Teleph Corp <Ntt> 背景画像抽出方法
US4847677A (en) * 1988-04-27 1989-07-11 Universal Video Communications Corp. Video telecommunication system and method for compressing and decompressing digital color video data
US5027413A (en) * 1988-06-17 1991-06-25 U.S. Philips Corp. Target detection systems
WO1991012584A2 (de) * 1990-02-09 1991-08-22 Siemens Aktiengesellschaft Verfahren zur bestimmung der momentanen lage und form von bewegten objekten und zu deren anzeige als binärbild
US5150432A (en) * 1990-03-26 1992-09-22 Kabushiki Kaisha Toshiba Apparatus for encoding/decoding video signals to improve quality of a specific region
WO1992003801A1 (en) * 1990-08-24 1992-03-05 The Board Of Regents Of The University Of Oklahoma Method and apparatus for detecting and quantifying motion of a body part
JPH05225341A (ja) * 1992-02-13 1993-09-03 Matsushita Electric Ind Co Ltd 移動物体検出装置
JPH0622318A (ja) * 1992-05-18 1994-01-28 Mitsubishi Electric Corp 移動物体抽出装置
JPH0652311A (ja) * 1992-07-31 1994-02-25 Kubota Corp 画像処理方法

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
M. Kaneta et al., Image Processing Method for Intruder Detection Around Power Line Towers, IEICE Transactions on Information and Systems, Oct. 1993, pp. 1153 1161. *
M. Kaneta et al., Image Processing Method for Intruder Detection Around Power Line Towers, IEICE Transactions on Information and Systems, Oct. 1993, pp. 1153-1161.
R.D. Horton, A Target Cueing and Tracking System (TCATS) for Smart Video Processing, Proceedings The Institute of Electrical and Electronic Engineers, 1990 International Carnahan Conference on Security Technology: Crime Countermeasures, Oct. 10 12, 1990, pp. 68 72. *
R.D. Horton, A Target Cueing and Tracking System (TCATS) for Smart Video Processing, Proceedings The Institute of Electrical and Electronic Engineers, 1990 International Carnahan Conference on Security Technology: Crime Countermeasures, Oct. 10-12, 1990, pp. 68-72.
X. Yuan et al., A Computer Vision System for Measurement of Pedestrian Volume, Proceedings of the Region Ten Conference (TENCON), Oct. 19 21, 1993, pp. 1046 1049. *
X. Yuan et al., A Computer Vision System for Measurement of Pedestrian Volume, Proceedings of the Region Ten Conference (TENCON), Oct. 19-21, 1993, pp. 1046-1049.

Cited By (304)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020030739A1 (en) * 1995-02-17 2002-03-14 Shigeki Nagaya Moving object detection apparatus
US20040046896A1 (en) * 1995-05-26 2004-03-11 Canon Kabushiki Kaisha Image processing apparatus and method
US6259827B1 (en) * 1996-03-21 2001-07-10 Cognex Corporation Machine vision methods for enhancing the contrast between an object and its background using multiple on-axis images
US5991428A (en) * 1996-09-12 1999-11-23 Kabushiki Kaisha Toshiba Moving object detection apparatus and method
US6603880B2 (en) 1997-10-03 2003-08-05 Nec Corporation Method and device of object detectable and background removal, and storage media for storing program thereof
US6333993B1 (en) * 1997-10-03 2001-12-25 Nec Corporation Method and device of object detectable and background removal, and storage media for storing program thereof
US8059866B2 (en) * 1997-12-23 2011-11-15 Intel Corporation Image selection based on image content
US20090279779A1 (en) * 1997-12-23 2009-11-12 Intel Corporation Image Selection Based on Image Content
US6184858B1 (en) * 1998-02-06 2001-02-06 Compaq Computer Corporation Technique for updating a background image
US6075535A (en) * 1998-06-26 2000-06-13 Hewlett-Packard Company Method and apparatus for visualizing the tile access frequencies for tiled, multi-resolution images
US6546115B1 (en) * 1998-09-10 2003-04-08 Hitachi Denshi Kabushiki Kaisha Method of updating reference background image, method of detecting entering objects and system for detecting entering objects using the methods
US7013035B2 (en) * 1998-09-25 2006-03-14 Canon Kabushiki Kaisha Image processing method for setting an extraction area, and apparatus and recording medium
US6901165B1 (en) * 1998-09-30 2005-05-31 Siemens Aktiengesellschaft Method of automatically triggering pattern recognition procedures
US6950130B1 (en) 1999-01-05 2005-09-27 Sharp Laboratories Of America, Inc. Method of image background replacement
US6681058B1 (en) * 1999-04-15 2004-01-20 Sarnoff Corporation Method and apparatus for estimating feature values in a region of a sequence of images
AU774180B2 (en) * 1999-05-28 2004-06-17 It Brokerage Services Pty Limited Method and apparatus for tracking a moving object
WO2000073996A1 (en) * 1999-05-28 2000-12-07 Glebe Systems Pty Ltd Method and apparatus for tracking a moving object
US7391444B1 (en) * 1999-07-23 2008-06-24 Sharp Kabushiki Kaisha Image pickup apparatus capable of selecting output according to time measured by timer
US6931146B2 (en) * 1999-12-20 2005-08-16 Fujitsu Limited Method and apparatus for detecting moving object
US20010004400A1 (en) * 1999-12-20 2001-06-21 Takahiro Aoki Method and apparatus for detecting moving object
US6798908B2 (en) * 1999-12-27 2004-09-28 Hitachi, Ltd. Surveillance apparatus and recording medium recorded surveillance program
US20010005219A1 (en) * 1999-12-27 2001-06-28 Hideaki Matsuo Human tracking device, human tracking method and recording medium recording program thereof
US6704433B2 (en) * 1999-12-27 2004-03-09 Matsushita Electric Industrial Co., Ltd. Human tracking device, human tracking method and recording medium recording program thereof
US6798909B2 (en) * 1999-12-27 2004-09-28 Hitachi, Ltd. Surveillance apparatus and recording medium recorded surveillance program
US6731799B1 (en) * 2000-06-01 2004-05-04 University Of Washington Object segmentation with background extraction and moving boundary techniques
US20020057343A1 (en) * 2000-06-30 2002-05-16 Ronk Lawrence J. Image object ranking
US7386170B2 (en) * 2000-06-30 2008-06-10 Texas Instruments Incorporated Image object ranking
US7058221B1 (en) * 2000-07-07 2006-06-06 Tani Electronics Industry Co., Ltd. Method of recognizing object based on pattern matching and medium for recording computer program having same
US20020051579A1 (en) * 2000-09-21 2002-05-02 Jacques Dugue Method and device for characterizing or controlling zones of temporal fluctuations of a scene
US7013022B2 (en) * 2000-09-21 2006-03-14 L'air Liquide, Societe Anonyme Pour L'etude Et L'exploitation Des Procedes Georges Claude Method and device for characterizing or controlling zones of temporal fluctuations of a scene
WO2002031751A1 (en) 2000-10-10 2002-04-18 Lockheed Martin Balanced object tracker in an image sequence
US6445832B1 (en) 2000-10-10 2002-09-03 Lockheed Martin Corporation Balanced template tracker for tracking an object image sequence
US20110007946A1 (en) * 2000-11-24 2011-01-13 Clever Sys, Inc. Unified system and method for animal behavior characterization with training capabilities
US20090296992A1 (en) * 2000-11-24 2009-12-03 Clever Sys, Inc. Unified system and method for animal behavior characterization from top view using video analysis
US20040141635A1 (en) * 2000-11-24 2004-07-22 Yiqing Liang Unified system and method for animal behavior characterization from top view using video analysis
US7817824B2 (en) 2000-11-24 2010-10-19 Clever Sys, Inc. Unified system and method for animal behavior characterization from top view using video analysis
US20070229522A1 (en) * 2000-11-24 2007-10-04 Feng-Feng Wang System and method for animal gait characterization from bottom view using video analysis
US7643655B2 (en) 2000-11-24 2010-01-05 Clever Sys, Inc. System and method for animal seizure detection and classification using video analysis
US20060110049A1 (en) * 2000-11-24 2006-05-25 Clever Sys, Inc. System and method for animal seizure detection and classification using video analysis
US20090285452A1 (en) * 2000-11-24 2009-11-19 Clever Sys, Inc. Unified system and method for animal behavior characterization in home cages using video analysis
US8514236B2 (en) 2000-11-24 2013-08-20 Cleversys, Inc. System and method for animal gait characterization from bottom view using video analysis
US7024020B2 (en) 2001-01-20 2006-04-04 Samsung Electronics Co., Ltd. Apparatus and method for generating object-labeled image in video sequence
US6990233B2 (en) * 2001-01-20 2006-01-24 Samsung Electronics Co., Ltd. Apparatus and method for extracting object based on feature matching between segmented regions in images
US20020136449A1 (en) * 2001-01-20 2002-09-26 Samsung Electronics Co., Ltd. Apparatus and method for extracting object based on feature matching between segmented regions in images
US20020097893A1 (en) * 2001-01-20 2002-07-25 Lee Seong-Deok Apparatus and method for generating object-labeled image in video sequence
US7079580B2 (en) * 2001-03-22 2006-07-18 Sony Corporation Moving picture encoder, moving picture encoding method, moving picture encoding program used therewith, and storage medium storing the same
US20020168009A1 (en) * 2001-03-22 2002-11-14 Tatsumi Sakaguchi Moving picture encoder, moving picture encoding method, moving picture encoding program used therewith, and storage medium storing the same
US7882135B2 (en) 2001-05-15 2011-02-01 Psychogenics, Inc. Method for predicting treatment classes using behavior informatics
US7580798B2 (en) 2001-05-15 2009-08-25 Psychogenics, Inc. Method for predicting treatment classes using animal behavior informatics
US20030004652A1 (en) * 2001-05-15 2003-01-02 Daniela Brunner Systems and methods for monitoring behavior informatics
US20030028327A1 (en) * 2001-05-15 2003-02-06 Daniela Brunner Systems and methods for monitoring behavior informatics
US20030083822A2 (en) * 2001-05-15 2003-05-01 Psychogenics, Inc. Systems and methods for monitoring behavior informatics
US20030100998A2 (en) * 2001-05-15 2003-05-29 Carnegie Mellon University (Pittsburgh, Pa) And Psychogenics, Inc. (Hawthorne, Ny) Systems and methods for monitoring behavior informatics
US20100106743A1 (en) * 2001-05-15 2010-04-29 Psychogenics, Inc. Method for Predicting Treatment Classes Using Behavior Informatics
US7269516B2 (en) 2001-05-15 2007-09-11 Psychogenics, Inc. Systems and methods for monitoring behavior informatics
US6912313B2 (en) 2001-05-31 2005-06-28 Sharp Laboratories Of America, Inc. Image background replacement method
US20020186881A1 (en) * 2001-05-31 2002-12-12 Baoxin Li Image background replacement method
US20030118233A1 (en) * 2001-11-20 2003-06-26 Andreas Olsson Method and device for identifying objects in digital images
US7283676B2 (en) * 2001-11-20 2007-10-16 Anoto Ab Method and device for identifying objects in digital images
US8027522B2 (en) 2002-02-27 2011-09-27 Nec Corporation Image recognition system and recognition method thereof and program
US20030161504A1 (en) * 2002-02-27 2003-08-28 Nec Corporation Image recognition system and recognition method thereof, and program
US7532745B2 (en) * 2002-02-27 2009-05-12 Nec Corporation Image recognition system and recognition method thereof, and program
US7184590B2 (en) 2002-07-17 2007-02-27 Lockheed Martin Corporation Algorithm selector
US20040013300A1 (en) * 2002-07-17 2004-01-22 Lee Harry C. Algorithm selector
US20040042674A1 (en) * 2002-09-02 2004-03-04 Canon Kabushiki Kaisha Image processing apparatus and method
US7257265B2 (en) * 2002-09-02 2007-08-14 Canon Kabushiki Kaisha Image processing apparatus and method
US20040105856A1 (en) * 2002-12-02 2004-06-03 Robin Thurmond Use of histamine H4 receptor antagonist for the treatment of inflammatory responses
US20040141633A1 (en) * 2003-01-21 2004-07-22 Minolta Co., Ltd. Intruding object detection device using background difference method
US7590261B1 (en) 2003-07-31 2009-09-15 Videomining Corporation Method and system for event detection by analysis of linear feature occlusion
US8060364B2 (en) * 2003-11-05 2011-11-15 Nice Systems, Ltd. Apparatus and method for event-driven content analysis
US20050108775A1 (en) * 2003-11-05 2005-05-19 Nice System Ltd Apparatus and method for event-driven content analysis
US20050254728A1 (en) * 2004-05-13 2005-11-17 Zhuo-Ya Wang Automatic cutting method for digital images
US7865834B1 (en) 2004-06-25 2011-01-04 Apple Inc. Multi-way video conferencing user interface
US20060045185A1 (en) * 2004-08-31 2006-03-02 Ramot At Tel-Aviv University Ltd. Apparatus and methods for the detection of abnormal motion in a video stream
US20060072101A1 (en) * 2004-10-05 2006-04-06 Young-Kug Park System and method for measuring tip velocity of sprayed fuel
US7405813B2 (en) * 2004-10-05 2008-07-29 Hyundai Motor Company System and method for measuring tip velocity of sprayed fuel
CN1766929B (zh) * 2004-10-29 2010-05-12 中国科学院计算技术研究所 一种基于三维数据库的运动对象运动重构方法
US20060126956A1 (en) * 2004-12-14 2006-06-15 Lg Electronics Inc. Method of coding and decoding image
US7903141B1 (en) 2005-02-15 2011-03-08 Videomining Corporation Method and system for event detection by multi-scale image invariant analysis
US20110064142A1 (en) * 2005-03-31 2011-03-17 Apple Inc. Encoding a Transparency (ALPHA) Channel in a Video Bitstream
US8830262B2 (en) 2005-03-31 2014-09-09 Apple Inc. Encoding a transparency (ALPHA) channel in a video bitstream
US7852353B1 (en) 2005-03-31 2010-12-14 Apple Inc. Encoding a transparency (alpha) channel in a video bitstream
US20100157049A1 (en) * 2005-04-03 2010-06-24 Igal Dvir Apparatus And Methods For The Semi-Automatic Tracking And Examining Of An Object Or An Event In A Monitored Site
US10019877B2 (en) 2005-04-03 2018-07-10 Qognify Ltd. Apparatus and methods for the semi-automatic tracking and examining of an object or an event in a monitored site
US20060245618A1 (en) * 2005-04-29 2006-11-02 Honeywell International Inc. Motion detection in a video stream
US9871963B2 (en) 2005-06-15 2018-01-16 Apple Inc. Image capture using display device as light source
US9413978B2 (en) 2005-06-15 2016-08-09 Apple Inc. Image capture using display device as light source
US8970776B2 (en) 2005-06-15 2015-03-03 Apple Inc. Image capture using display device as light source
US20060284895A1 (en) * 2005-06-15 2006-12-21 Marcu Gabriel G Dynamic gamma correction
US20070071296A1 (en) * 2005-09-28 2007-03-29 Ryosuke Nonaka Radiographic image processing apparatus for processing radiographic image taken with radiation, method of radiographic image processing, and computer program product therefor
US8085318B2 (en) 2005-10-11 2011-12-27 Apple Inc. Real-time image capture and manipulation based on streaming data
US7663691B2 (en) 2005-10-11 2010-02-16 Apple Inc. Image capture using display device as light source
US20070081094A1 (en) * 2005-10-11 2007-04-12 Jean-Pierre Ciudad Image capture
US10397470B2 (en) 2005-10-11 2019-08-27 Apple Inc. Image capture using display device as light source
US8537248B2 (en) 2005-10-11 2013-09-17 Apple Inc. Image capture and manipulation
US20100118179A1 (en) * 2005-10-11 2010-05-13 Apple Inc. Image Capture Using Display Device As Light Source
US8199249B2 (en) 2005-10-11 2012-06-12 Apple Inc. Image capture using display device as light source
US20070081740A1 (en) * 2005-10-11 2007-04-12 Jean-Pierre Ciudad Image capture and manipulation
US20070230744A1 (en) * 2006-03-29 2007-10-04 Mark Dronge Security alarm system
US7864983B2 (en) 2006-03-29 2011-01-04 Mark Dronge Security alarm system
US7526105B2 (en) 2006-03-29 2009-04-28 Mark Dronge Security alarm system
US20090225166A1 (en) * 2006-03-29 2009-09-10 Mark Dronge Security Alarm System
US20070274402A1 (en) * 2006-05-23 2007-11-29 Honeywell International Inc. Application of short term and long term background scene dynamics in motion detection
US20070291135A1 (en) * 2006-06-20 2007-12-20 Baer Richard L Motion characterization sensor
US9142254B2 (en) 2006-07-20 2015-09-22 Adobe Systems Incorporated Capturing frames from an external source
US7925978B1 (en) * 2006-07-20 2011-04-12 Adobe Systems Incorporated Capturing frames from an external source
US9576388B2 (en) 2006-08-04 2017-02-21 Apple Inc. Framework for graphics animation and compositing operations
US9852535B2 (en) 2006-08-04 2017-12-26 Apple Inc. Framework for graphics animation and compositing operations
US11222456B2 (en) 2006-08-04 2022-01-11 Apple Inc. Frameworks for graphics animation and compositing operations
US9424675B2 (en) 2006-08-04 2016-08-23 Apple, Inc. Framework for graphics animation and compositing operations
US9019300B2 (en) 2006-08-04 2015-04-28 Apple Inc. Framework for graphics animation and compositing operations
US20080030504A1 (en) * 2006-08-04 2008-02-07 Apple Inc. Framework for Graphics Animation and Compositing Operations
US8130226B2 (en) 2006-08-04 2012-03-06 Apple Inc. Framework for graphics animation and compositing operations
US10521949B2 (en) 2006-08-04 2019-12-31 Apple Inc. Framework for graphics animation and compositing operations
US20080034292A1 (en) * 2006-08-04 2008-02-07 Apple Computer, Inc. Framework for graphics animation and compositing operations
US20090190799A1 (en) * 2006-09-20 2009-07-30 Forschungszentrum Karlsruhe Gmbh Method for characterizing the exhaust gas burn-off quality in combustion systems
US8447068B2 (en) * 2006-09-20 2013-05-21 Forschungszentrum Karlsruhe Gmbh Method for characterizing the exhaust gas burn-off quality in combustion systems
US8878857B2 (en) 2006-11-17 2014-11-04 Apple Inc. Methods and apparatuses for expressing animation in a data stream
US20080120626A1 (en) * 2006-11-17 2008-05-22 Peter Graffagnino Methods and apparatuses for providing a hardware accelerated web engine
US9953391B2 (en) 2006-11-17 2018-04-24 Apple Inc. Methods and apparatuses for providing a hardware accelerated web engine
US10497086B2 (en) 2006-11-17 2019-12-03 Apple Inc. Methods and apparatuses for providing a hardware accelerated web engine
US8234392B2 (en) 2006-11-17 2012-07-31 Apple Inc. Methods and apparatuses for providing a hardware accelerated web engine
US8620028B2 (en) 2007-02-08 2013-12-31 Behavioral Recognition Systems, Inc. Behavioral recognition system
US20080199095A1 (en) * 2007-02-20 2008-08-21 Microsoft Corporation Pixel Extraction And Replacement
US7920717B2 (en) * 2007-02-20 2011-04-05 Microsoft Corporation Pixel extraction and replacement
US20080307307A1 (en) * 2007-06-08 2008-12-11 Jean-Pierre Ciudad Image capture and manipulation
US20080303949A1 (en) * 2007-06-08 2008-12-11 Apple Inc. Manipulating video streams
US8122378B2 (en) 2007-06-08 2012-02-21 Apple Inc. Image capture and manipulation
US20080310677A1 (en) * 2007-06-18 2008-12-18 Weismuller Thomas P Object detection system and method incorporating background clutter removal
US10706284B2 (en) 2007-07-11 2020-07-07 Avigilon Patent Holding 1 Corporation Semantic representation module of a machine-learning engine in a video analysis system
US9946934B2 (en) 2007-07-11 2018-04-17 Avigilon Patent Holding 1 Corporation Semantic representation module of a machine-learning engine in a video analysis system
US8411935B2 (en) 2007-07-11 2013-04-02 Behavioral Recognition Systems, Inc. Semantic representation module of a machine-learning engine in a video analysis system
US8189905B2 (en) 2007-07-11 2012-05-29 Behavioral Recognition Systems, Inc. Cognitive model for a machine-learning engine in a video analysis system
US9235752B2 (en) 2007-07-11 2016-01-12 9051147 Canada Inc. Semantic representation module of a machine-learning engine in a video analysis system
US9665774B2 (en) 2007-07-11 2017-05-30 Avigilon Patent Holding 1 Corporation Semantic representation module of a machine-learning engine in a video analysis system
US20090016599A1 (en) * 2007-07-11 2009-01-15 John Eric Eaton Semantic representation module of a machine-learning engine in a video analysis system
US10423835B2 (en) 2007-07-11 2019-09-24 Avigilon Patent Holding 1 Corporation Semantic representation module of a machine-learning engine in a video analysis system
US20090016600A1 (en) * 2007-07-11 2009-01-15 John Eric Eaton Cognitive model for a machine-learning engine in a video analysis system
US9489569B2 (en) 2007-07-11 2016-11-08 9051147 Canada Inc. Semantic representation module of a machine-learning engine in a video analysis system
US10198636B2 (en) 2007-07-11 2019-02-05 Avigilon Patent Holding 1 Corporation Semantic representation module of a machine-learning engine in a video analysis system
US8762864B2 (en) 2007-08-06 2014-06-24 Apple Inc. Background removal tool for a presentation application
US9619471B2 (en) 2007-08-06 2017-04-11 Apple Inc. Background removal tool for a presentation application
US8559732B2 (en) 2007-08-06 2013-10-15 Apple Inc. Image foreground extraction using a presentation application
US20090044136A1 (en) * 2007-08-06 2009-02-12 Apple Inc. Background removal tool for a presentation application
US20090070636A1 (en) * 2007-08-06 2009-03-12 Apple Inc. Advanced import/export panel notifications using a presentation application
US9430479B2 (en) 2007-08-06 2016-08-30 Apple Inc. Interactive frames for images and videos displayed in a presentation application
US9189875B2 (en) 2007-08-06 2015-11-17 Apple Inc. Advanced import/export panel notifications using a presentation application
US20090144651A1 (en) * 2007-08-06 2009-06-04 Apple Inc. Interactive frames for images and videos displayed in a presentation application
US20090044117A1 (en) * 2007-08-06 2009-02-12 Apple Inc. Recording and exporting slide show presentations using a presentation application
US8225208B2 (en) 2007-08-06 2012-07-17 Apple Inc. Interactive frames for images and videos displayed in a presentation application
US20090060334A1 (en) * 2007-08-06 2009-03-05 Apple Inc. Image foreground extraction using a presentation application
WO2009026966A1 (en) * 2007-08-31 2009-03-05 Siemens Building Technologies Fire & Security Products Gmbh & Co.Ohg Method of estimating illumination change of images for object detection
US20090066802A1 (en) * 2007-09-06 2009-03-12 Suguru Itagaki Image processing device and method
US8175333B2 (en) 2007-09-27 2012-05-08 Behavioral Recognition Systems, Inc. Estimator identifier component for behavioral recognition system
US20090087024A1 (en) * 2007-09-27 2009-04-02 John Eric Eaton Context processor for video analysis system
US8200011B2 (en) 2007-09-27 2012-06-12 Behavioral Recognition Systems, Inc. Context processor for video analysis system
US8705861B2 (en) 2007-09-27 2014-04-22 Behavioral Recognition Systems, Inc. Context processor for video analysis system
US20090087085A1 (en) * 2007-09-27 2009-04-02 John Eric Eaton Tracker component for behavioral recognition system
US8300924B2 (en) 2007-09-27 2012-10-30 Behavioral Recognition Systems, Inc. Tracker component for behavioral recognition system
US20090087027A1 (en) * 2007-09-27 2009-04-02 John Eric Eaton Estimator identifier component for behavioral recognition system
US20100182433A1 (en) * 2007-10-17 2010-07-22 Hitachi Kokusai Electric, Inc. Object detection system
US8233047B2 (en) * 2007-10-17 2012-07-31 Hitachi Kokusai Electric Inc. Object detection system
US20090279738A1 (en) * 2008-05-08 2009-11-12 Denso Corporation Apparatus for image recognition
US8238606B2 (en) 2008-05-08 2012-08-07 Denso Corporation Apparatus for image recognition
US20090324011A1 (en) * 2008-06-25 2009-12-31 Lin Daw-Tung Method of detecting moving object
US8126212B2 (en) 2008-06-25 2012-02-28 Natinoal Chiao Tung University Method of detecting moving object
US11468660B2 (en) 2008-09-11 2022-10-11 Intellective Ai, Inc. Pixel-level based micro-feature extraction
US10755131B2 (en) 2008-09-11 2020-08-25 Intellective Ai, Inc. Pixel-level based micro-feature extraction
US20110044536A1 (en) * 2008-09-11 2011-02-24 Wesley Kenneth Cobb Pixel-level based micro-feature extraction
US9633275B2 (en) 2008-09-11 2017-04-25 Wesley Kenneth Cobb Pixel-level based micro-feature extraction
US8634635B2 (en) 2008-10-30 2014-01-21 Clever Sys, Inc. System and method for stereo-view multiple animal behavior characterization
US20100111359A1 (en) * 2008-10-30 2010-05-06 Clever Sys, Inc. System and method for stereo-view multiple animal behavior characterization
US9373055B2 (en) * 2008-12-16 2016-06-21 Behavioral Recognition Systems, Inc. Hierarchical sudden illumination change detection using radiance consistency within a spatial neighborhood
US20100150471A1 (en) * 2008-12-16 2010-06-17 Wesley Kenneth Cobb Hierarchical sudden illumination change detection using radiance consistency within a spatial neighborhood
US20100208986A1 (en) * 2009-02-18 2010-08-19 Wesley Kenneth Cobb Adaptive update of background pixel thresholds using sudden illumination change detection
US8285046B2 (en) 2009-02-18 2012-10-09 Behavioral Recognition Systems, Inc. Adaptive update of background pixel thresholds using sudden illumination change detection
US20100260376A1 (en) * 2009-04-14 2010-10-14 Wesley Kenneth Cobb Mapper component for multiple art networks in a video analysis system
US8416296B2 (en) 2009-04-14 2013-04-09 Behavioral Recognition Systems, Inc. Mapper component for multiple art networks in a video analysis system
US20110043536A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Visualizing and updating sequences and segments in a video surveillance system
US20110044492A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Adaptive voting experts for incremental segmentation of sequences with prediction in a video surveillance system
US10248869B2 (en) 2009-08-18 2019-04-02 Omni Ai, Inc. Scene preset identification using quadtree decomposition analysis
US8493409B2 (en) 2009-08-18 2013-07-23 Behavioral Recognition Systems, Inc. Visualizing and updating sequences and segments in a video surveillance system
US8295591B2 (en) 2009-08-18 2012-10-23 Behavioral Recognition Systems, Inc. Adaptive voting experts for incremental segmentation of sequences with prediction in a video surveillance system
US8340352B2 (en) 2009-08-18 2012-12-25 Behavioral Recognition Systems, Inc. Inter-trajectory anomaly detection using adaptive voting experts in a video surveillance system
US8625884B2 (en) 2009-08-18 2014-01-07 Behavioral Recognition Systems, Inc. Visualizing and updating learned event maps in surveillance systems
US8280153B2 (en) 2009-08-18 2012-10-02 Behavioral Recognition Systems Visualizing and updating learned trajectories in video surveillance systems
US20110044498A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Visualizing and updating learned trajectories in video surveillance systems
US20110043625A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Scene preset identification using quadtree decomposition analysis
US20110043689A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Field-of-view change detection
US9959630B2 (en) 2009-08-18 2018-05-01 Avigilon Patent Holding 1 Corporation Background model for complex and dynamic scenes
US20110044537A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Background model for complex and dynamic scenes
US8379085B2 (en) 2009-08-18 2013-02-19 Behavioral Recognition Systems, Inc. Intra-trajectory anomaly detection using adaptive voting experts in a video surveillance system
US10796164B2 (en) 2009-08-18 2020-10-06 Intellective Ai, Inc. Scene preset identification using quadtree decomposition analysis
US20110044533A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Visualizing and updating learned event maps in surveillance systems
US8358834B2 (en) 2009-08-18 2013-01-22 Behavioral Recognition Systems Background model for complex and dynamic scenes
US10032282B2 (en) 2009-08-18 2018-07-24 Avigilon Patent Holding 1 Corporation Background model for complex and dynamic scenes
US20110043626A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Intra-trajectory anomaly detection using adaptive voting experts in a video surveillance system
US20110044499A1 (en) * 2009-08-18 2011-02-24 Wesley Kenneth Cobb Inter-trajectory anomaly detection using adaptive voting experts in a video surveillance system
US9805271B2 (en) 2009-08-18 2017-10-31 Omni Ai, Inc. Scene preset identification using quadtree decomposition analysis
US20110050897A1 (en) * 2009-08-31 2011-03-03 Wesley Kenneth Cobb Visualizing and updating classifications in a video surveillance system
US20110052067A1 (en) * 2009-08-31 2011-03-03 Wesley Kenneth Cobb Clustering nodes in a self-organizing map using an adaptive resonance theory network
US8270733B2 (en) 2009-08-31 2012-09-18 Behavioral Recognition Systems, Inc. Identifying anomalous object types during classification
US8270732B2 (en) 2009-08-31 2012-09-18 Behavioral Recognition Systems, Inc. Clustering nodes in a self-organizing map using an adaptive resonance theory network
US8285060B2 (en) 2009-08-31 2012-10-09 Behavioral Recognition Systems, Inc. Detecting anomalous trajectories in a video surveillance system
US20110052068A1 (en) * 2009-08-31 2011-03-03 Wesley Kenneth Cobb Identifying anomalous object types during classification
US20110050896A1 (en) * 2009-08-31 2011-03-03 Wesley Kenneth Cobb Visualizing and updating long-term memory percepts in a video surveillance system
US8167430B2 (en) 2009-08-31 2012-05-01 Behavioral Recognition Systems, Inc. Unsupervised learning of temporal anomalies for a video surveillance system
US20110052000A1 (en) * 2009-08-31 2011-03-03 Wesley Kenneth Cobb Detecting anomalous trajectories in a video surveillance system
US20110051992A1 (en) * 2009-08-31 2011-03-03 Wesley Kenneth Cobb Unsupervised learning of temporal anomalies for a video surveillance system
US10489679B2 (en) 2009-08-31 2019-11-26 Avigilon Patent Holding 1 Corporation Visualizing and updating long-term memory percepts in a video surveillance system
US8786702B2 (en) 2009-08-31 2014-07-22 Behavioral Recognition Systems, Inc. Visualizing and updating long-term memory percepts in a video surveillance system
US8797405B2 (en) 2009-08-31 2014-08-05 Behavioral Recognition Systems, Inc. Visualizing and updating classifications in a video surveillance system
US8218819B2 (en) 2009-09-01 2012-07-10 Behavioral Recognition Systems, Inc. Foreground object detection in a video surveillance system
US20110052002A1 (en) * 2009-09-01 2011-03-03 Wesley Kenneth Cobb Foreground object tracking
US8218818B2 (en) 2009-09-01 2012-07-10 Behavioral Recognition Systems, Inc. Foreground object tracking
US20110052003A1 (en) * 2009-09-01 2011-03-03 Wesley Kenneth Cobb Foreground object detection in a video surveillance system
US20120177121A1 (en) * 2009-09-04 2012-07-12 Stmicroelectronics Pvt. Ltd. Advance video coding with perceptual quality scalability for regions of interest
US10178396B2 (en) * 2009-09-04 2019-01-08 Stmicroelectronics International N.V. Object tracking
US9626769B2 (en) * 2009-09-04 2017-04-18 Stmicroelectronics International N.V. Digital video encoder system, method, and non-transitory computer-readable medium for tracking object regions
US9813731B2 (en) 2009-09-04 2017-11-07 Stmicroelectonics International N.V. System and method for object based parametric video coding
US8494222B2 (en) 2009-09-17 2013-07-23 Behavioral Recognition Systems, Inc. Classifier anomalies for observed behaviors in a video surveillance system
US8170283B2 (en) 2009-09-17 2012-05-01 Behavioral Recognition Systems Inc. Video surveillance system configured to analyze complex behaviors using alternating layers of clustering and sequencing
US20110064267A1 (en) * 2009-09-17 2011-03-17 Wesley Kenneth Cobb Classifier anomalies for observed behaviors in a video surveillance system
US20110064268A1 (en) * 2009-09-17 2011-03-17 Wesley Kenneth Cobb Video surveillance system configured to analyze complex behaviors using alternating layers of clustering and sequencing
US8180105B2 (en) 2009-09-17 2012-05-15 Behavioral Recognition Systems, Inc. Classifier anomalies for observed behaviors in a video surveillance system
US20110243451A1 (en) * 2010-03-30 2011-10-06 Hideki Oyaizu Image processing apparatus and method, and program
US8923555B2 (en) * 2010-10-29 2014-12-30 Keyence Corporation Image measuring device, image measuring method, and computer program
US20120106788A1 (en) * 2010-10-29 2012-05-03 Keyence Corporation Image Measuring Device, Image Measuring Method, And Computer Program
US20120169937A1 (en) * 2011-01-05 2012-07-05 Canon Kabushiki Kaisha Image processing apparatus and image processing method
US8582815B2 (en) 2011-02-23 2013-11-12 Denso Corporation Moving object detection apparatus
US9053385B2 (en) * 2011-12-19 2015-06-09 Panasonic Intellectual Property Management Co., Ltd. Object detection device and object detection method
US20150003743A1 (en) * 2011-12-19 2015-01-01 Panasonic Corporation Object detection device and object detection method
US11217088B2 (en) 2012-03-15 2022-01-04 Intellective Ai, Inc. Alert volume normalization in a video surveillance system
US9349275B2 (en) 2012-03-15 2016-05-24 Behavorial Recognition Systems, Inc. Alert volume normalization in a video surveillance system
US10096235B2 (en) 2012-03-15 2018-10-09 Omni Ai, Inc. Alert directives and focused alert directives in a behavioral recognition system
US9208675B2 (en) 2012-03-15 2015-12-08 Behavioral Recognition Systems, Inc. Loitering detection in a video surveillance system
US11727689B2 (en) 2012-03-15 2023-08-15 Intellective Ai, Inc. Alert directives and focused alert directives in a behavioral recognition system
US10848715B2 (en) 2012-06-29 2020-11-24 Intellective Ai, Inc. Anomalous stationary object detection and reporting
US9111148B2 (en) 2012-06-29 2015-08-18 Behavioral Recognition Systems, Inc. Unsupervised learning of feature anomalies for a video surveillance system
US9911043B2 (en) 2012-06-29 2018-03-06 Omni Ai, Inc. Anomalous object interaction detection and reporting
US10257466B2 (en) 2012-06-29 2019-04-09 Omni Ai, Inc. Anomalous stationary object detection and reporting
US10410058B1 (en) 2012-06-29 2019-09-10 Omni Ai, Inc. Anomalous object interaction detection and reporting
US9111353B2 (en) 2012-06-29 2015-08-18 Behavioral Recognition Systems, Inc. Adaptive illuminance filter in a video analysis system
US9317908B2 (en) 2012-06-29 2016-04-19 Behavioral Recognition System, Inc. Automatic gain control filter in a video analysis system
US11233976B2 (en) 2012-06-29 2022-01-25 Intellective Ai, Inc. Anomalous stationary object detection and reporting
US9113143B2 (en) 2012-06-29 2015-08-18 Behavioral Recognition Systems, Inc. Detecting and responding to an out-of-focus camera in a video analytics system
US9723271B2 (en) 2012-06-29 2017-08-01 Omni Ai, Inc. Anomalous stationary object detection and reporting
US11017236B1 (en) 2012-06-29 2021-05-25 Intellective Ai, Inc. Anomalous object interaction detection and reporting
US9104918B2 (en) 2012-08-20 2015-08-11 Behavioral Recognition Systems, Inc. Method and system for detecting sea-surface oil
US20140133753A1 (en) * 2012-11-09 2014-05-15 Ge Aviation Systems Llc Spectral scene simplification through background subtraction
US9674442B2 (en) 2012-11-12 2017-06-06 Omni Ai, Inc. Image stabilization techniques for video surveillance systems
US9232140B2 (en) 2012-11-12 2016-01-05 Behavioral Recognition Systems, Inc. Image stabilization techniques for video surveillance systems
US10827122B2 (en) 2012-11-12 2020-11-03 Intellective Ai, Inc. Image stabilization techniques for video
US10237483B2 (en) 2012-11-12 2019-03-19 Omni Ai, Inc. Image stabilization techniques for video surveillance systems
US9507768B2 (en) 2013-08-09 2016-11-29 Behavioral Recognition Systems, Inc. Cognitive information security using a behavioral recognition system
US11818155B2 (en) 2013-08-09 2023-11-14 Intellective Ai, Inc. Cognitive information security using a behavior recognition system
US9973523B2 (en) 2013-08-09 2018-05-15 Omni Ai, Inc. Cognitive information security using a behavioral recognition system
US9639521B2 (en) 2013-08-09 2017-05-02 Omni Ai, Inc. Cognitive neuro-linguistic behavior recognition system for multi-sensor data fusion
US10735446B2 (en) 2013-08-09 2020-08-04 Intellective Ai, Inc. Cognitive information security using a behavioral recognition system
US11991194B2 (en) 2013-08-09 2024-05-21 Intellective Ai, Inc. Cognitive neuro-linguistic behavior recognition system for multi-sensor data fusion
US10187415B2 (en) 2013-08-09 2019-01-22 Omni Ai, Inc. Cognitive information security using a behavioral recognition system
US20150254513A1 (en) * 2014-03-10 2015-09-10 Mitsubishi Electric Research Laboratories, Inc. Method for Extracting Low-Rank Descriptors from Images and Videos for Querying, Classification, and Object Detection
US9639761B2 (en) * 2014-03-10 2017-05-02 Mitsubishi Electric Research Laboratories, Inc. Method for extracting low-rank descriptors from images and videos for querying, classification, and object detection
US20170280154A1 (en) * 2014-09-25 2017-09-28 Sony Semiconductor Solutions Corporation Signal processing apparatus, imaging apparatus, and signal processing method
US10531112B2 (en) * 2014-09-25 2020-01-07 Sony Semiconductor Solutions Corporation Signal processing apparatus, imaging apparatus, and signal processing method to reduce electric power required for signal processing
US20180211397A1 (en) * 2014-10-27 2018-07-26 Playsight Interactive Ltd. Object extraction from video images system and method
US20170200281A1 (en) * 2014-10-27 2017-07-13 Playsight Interactive Ltd. Object extraction from video images system and method
US20160117842A1 (en) * 2014-10-27 2016-04-28 Playsight Enteractive Ltd. Object extraction from video images
US9639954B2 (en) * 2014-10-27 2017-05-02 Playsigh Interactive Ltd. Object extraction from video images
US9959632B2 (en) * 2014-10-27 2018-05-01 Playsight Interactive Ltd. Object extraction from video images system and method
US10916039B2 (en) 2014-10-29 2021-02-09 Intellective Ai, Inc. Background foreground model with dynamic absorption window and incremental update for background model thresholds
US9471844B2 (en) 2014-10-29 2016-10-18 Behavioral Recognition Systems, Inc. Dynamic absorption window for foreground background detector
US9460522B2 (en) 2014-10-29 2016-10-04 Behavioral Recognition Systems, Inc. Incremental update for background model thresholds
US10872243B2 (en) * 2014-10-29 2020-12-22 Intellective Ai, Inc. Foreground detector for video analytics system
US20190311204A1 (en) * 2014-10-29 2019-10-10 Omni Ai, Inc. Foreground detector for video analytics system
US9349054B1 (en) 2014-10-29 2016-05-24 Behavioral Recognition Systems, Inc. Foreground detector for video analytics system
US10373340B2 (en) 2014-10-29 2019-08-06 Omni Ai, Inc. Background foreground model with dynamic absorption window and incremental update for background model thresholds
US10303955B2 (en) * 2014-10-29 2019-05-28 Omni Al, Inc. Foreground detector for video analytics system
US20180082130A1 (en) * 2014-10-29 2018-03-22 Omni Ai, Inc. Foreground detector for video analytics system
US20180296183A1 (en) * 2014-11-04 2018-10-18 Vib Vzw Method and apparatus for ultrasound imaging of brain activity
US10409910B2 (en) 2014-12-12 2019-09-10 Omni Ai, Inc. Perceptual associative memory for a neuro-linguistic behavior recognition system
US10409909B2 (en) 2014-12-12 2019-09-10 Omni Ai, Inc. Lexical analyzer for a neuro-linguistic behavior recognition system
US11847413B2 (en) 2014-12-12 2023-12-19 Intellective Ai, Inc. Lexical analyzer for a neuro-linguistic behavior recognition system
US11017168B2 (en) 2014-12-12 2021-05-25 Intellective Ai, Inc. Lexical analyzer for a neuro-linguistic behavior recognition system
US20170154427A1 (en) * 2015-11-30 2017-06-01 Raytheon Company System and Method for Generating a Background Reference Image from a Series of Images to Facilitate Moving Object Identification
US9710911B2 (en) * 2015-11-30 2017-07-18 Raytheon Company System and method for generating a background reference image from a series of images to facilitate moving object identification
US20170337431A1 (en) * 2016-05-18 2017-11-23 Canon Kabushiki Kaisha Image processing apparatus and method and monitoring system
US10445590B2 (en) * 2016-05-18 2019-10-15 Canon Kabushiki Kaisha Image processing apparatus and method and monitoring system
CN107404628B (zh) * 2016-05-18 2020-09-01 佳能株式会社 图像处理装置及方法以及监视系统
CN107404628A (zh) * 2016-05-18 2017-11-28 佳能株式会社 图像处理装置及方法以及监视系统
US10643076B2 (en) 2016-07-01 2020-05-05 International Business Machines Corporation Counterfeit detection
US20190370977A1 (en) * 2017-01-30 2019-12-05 Nec Corporation Moving object detection apparatus, moving object detection method and program
US10853950B2 (en) * 2017-01-30 2020-12-01 Nec Corporation Moving object detection apparatus, moving object detection method and program
US10755419B2 (en) * 2017-01-30 2020-08-25 Nec Corporation Moving object detection apparatus, moving object detection method and program
US10769798B2 (en) * 2017-01-30 2020-09-08 Nec Corporation Moving object detection apparatus, moving object detection method and program
DE102017011604A1 (de) 2017-12-14 2019-06-19 Kuka Deutschland Gmbh Verfahren und System zum Erstellen eines Modells
WO2019115198A1 (de) 2017-12-14 2019-06-20 Kuka Deutschland Gmbh Verfahren und system zum erstellen eines modells
US10410371B2 (en) 2017-12-21 2019-09-10 The Boeing Company Cluttered background removal from imagery for object detection
US11461903B2 (en) 2018-05-24 2022-10-04 Nippon Telegraph And Telephone Corporation Video processing device, video processing method, and video processing program
US11288820B2 (en) 2018-06-09 2022-03-29 Lot Spot Inc. System and method for transforming video data into directional object count
US20220164965A1 (en) * 2018-06-09 2022-05-26 Lot Spot Inc. System and method for transforming video data into directional object count
US11276186B2 (en) * 2018-06-14 2022-03-15 Canon Kabushiki Kaisha Image processing apparatus, image capturing apparatus, image processing method, and non-transitory computer-readable storage medium
US11127141B2 (en) * 2018-11-27 2021-09-21 Canon Kabushiki Kaisha Image processing apparatus, image processing method, and a non-transitory computer readable storage medium
WO2021001323A1 (en) 2019-07-01 2021-01-07 Thales Dis France Sa Method to generate a slap/fingers foreground mask

Also Published As

Publication number Publication date
DE69510252D1 (de) 1999-07-22
JPH07302328A (ja) 1995-11-14
EP0671706A3 (de) 1996-12-11
EP0671706A2 (de) 1995-09-13
EP0671706B1 (de) 1999-06-16
JP3123587B2 (ja) 2001-01-15
DE69510252T2 (de) 1999-11-11

Similar Documents

Publication Publication Date Title
US5748775A (en) Method and apparatus for moving object extraction based on background subtraction
US6141433A (en) System and method for segmenting image regions from a scene likely to represent particular objects in the scene
CN105678811B (zh) 一种基于运动检测的人体异常行为检测方法
Stander et al. Detection of moving cast shadows for object segmentation
Javed et al. Tracking and object classification for automated surveillance
Pless et al. Evaluation of local models of dynamic backgrounds
US7778445B2 (en) Method and system for the detection of removed objects in video images
EP1881454A1 (de) Bildverarbeitung für Änderungsdetektion
US7982774B2 (en) Image processing apparatus and image processing method
Rowe et al. Statistical mosaics for tracking
Ziliani et al. Image analysis for video surveillance based on spatial regularization of a statistical model-based change detection
CN110400294B (zh) 一种红外目标探测系统及探测方法
Chen et al. Indoor and outdoor people detection and shadow suppression by exploiting HSV color information
WO1998028706B1 (en) Low false alarm rate video security system using object classification
Lian et al. A novel method on moving-objects detection based on background subtraction and three frames differencing
KR100217485B1 (ko) 동영상 부호기 또는 복호기에서 이동보상방법
CN111914627A (zh) 一种车辆识别与追踪方法及装置
CN113902694A (zh) 一种基于动静结合的目标检测方法
CN113065454A (zh) 一种高空抛物目标识别比较的方法及装置
CN116110006B (zh) 一种用于智慧旅游系统的景区游客异常行为识别方法
CN111640134B (zh) 人脸跟踪方法、装置、计算机设备及其存储装置
JPH05143737A (ja) 動きベクトルによる識別方法及び装置
Hötter et al. Detection and description of moving objects by stochastic modelling and analysis of complex scenes
CN116883897A (zh) 一种低分辨率目标识别方法
Kim et al. Unsupervised moving object segmentation and recognition using clustering and a neural network

Legal Events

Date Code Title Description
AS Assignment

Owner name: NIPPON TELEGRAPH AND TELEPHONE CORPORATION, JAPAN

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:TSUCHIKAWA, MEGUMU;SATO, ATSUSHI;REEL/FRAME:008795/0192

Effective date: 19950306

CC Certificate of correction
FPAY Fee payment

Year of fee payment: 4

FPAY Fee payment

Year of fee payment: 8

REMI Maintenance fee reminder mailed
LAPS Lapse for failure to pay maintenance fees
STCH Information on status: patent discontinuation

Free format text: PATENT EXPIRED DUE TO NONPAYMENT OF MAINTENANCE FEES UNDER 37 CFR 1.362

FP Lapsed due to failure to pay maintenance fee

Effective date: 20100505